Editing IPCC:AR6/WGII/Chapter-8 (section)

== 8.3 Human Vulnerability, Spatial Hotspots, Observed Loss and Damage, and Livelihood Challenges ==

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This section assesses the literature on vulnerability—the assessment of vulnerability at global and national scales—and explores economic and non-economic losses of people and livelihoods exposed to and impacted by climate change. The section examines how climate change threatens livelihoods and juxtaposes global and local level assessments of vulnerability based on empirical data at different scales. The analysis of recent literature underscores that climate change impacts and adaptation needs cannot be understood by looking at climate change only. Vulnerability and livelihood security are seen as an important component for understanding the human dimension of climate change ( [[#Rhiney--2016|Rhiney et al., 2016]] ; [[#Cardona--2017|Cardona, 2017]] ; [[#Byers--2018|Byers et al., 2018]] ; [[#Eriksen--2020|Eriksen et al., 2020]] ; [[#Wisner--2020|Wisner, 2020]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ; [[#Cole--2021|Cole et al., 2021]] ).

Linkages between global and individual vulnerability and livelihood security, including aspects of intersectionality are also assessed. Overall, this [[#8.3|Section 8.3]] reveals that different countries, societies and specific groups within a society have very different starting points on their move towards climate resilience.

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=== 8.3.1 Assessments of Risk and Vulnerability ===

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Conventional assessments of risks and the benefits of adaptation and risk reduction measures in the context of climate change primarily focus on the financial value of the avoided losses (in USD) and the assets that are going to be protected from adverse consequences of climate change or extreme events due to specific measures (e.g., dyke construction). Even though these assessments fall short of measuring the real costs of addressing climate change impacts (see [[#DeFries--2019|DeFries et al., 2019]] ), they often support the definition of priorities in terms of protecting economic values and assets. However, these assessments do not sufficiently account for how climate change impacts and imposes risks on poor people, nor do they capture issues of climate justice and more complex societal impacts and future risks. For example, various observed losses in the context of climate change cannot be sufficiently expressed in terms of an economic value (see [[#8.3.5|Section 8.3.5]] ), but these items or assets are highly relevant for various people with limited economic resources ( [[#Hallegatte--2017|Hallegatte et al., 2017]] ). Consequently, the assessment of risks from climate change facing particularly poor people requires comprehensive assessments of human vulnerability, resilience and the impacts of climate change on human well-being going beyond a simple temperature–societal-impact understanding. Knowledge about methods and approaches to assess human or human–environmental vulnerability and livelihood security, including aspects of intersectionality, is important in order to explore whether or not adaptation and development programmes are able to reduce vulnerability. The body of literature on these issues has grown significantly since the AR5 publication ( [[#IPCC--2014a|IPCC, 2014a]] ; [[#Moser--2014|Moser, 2014]] ).

This literature underscores that approaches to assess resilience, vulnerability and human well-being include global assessments that can inform strategies and priority settings for adaptation and risk reduction in the context of climate change ( ''high confidence'' ) ( [[#WHO--2014b|WHO, 2014b]] ; [[#Young--2015|Young et al., 2015]] ; [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ; [[#GIZ%20and%20BMZ--2017|GIZ and BMZ, 2017]] ; [[#Hallegatte--2017|Hallegatte et al., 2017]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ; [[#Garschagen--2021|Garschagen et al., 2021]] ; Toolkit, 2021).

These quantitative global assessments that have emerged within the last decades have not been sufficiently assessed in former IPCC reports, for example, in terms of the agreement on spatial hotspots or in terms of regional clusters of vulnerability and the linkages between past societal impacts and levels of vulnerability. The assessed literature shows that conditions and phenomena that characterise systemic vulnerability (hazard independent vulnerability), such as high levels of poverty and gender inequality, limited access to basic infrastructure services or state fragility are highly relevant for understanding societal impacts of climatic hazards and future risks of climate change (e.g., [[#Cutter--2003|Cutter et al., 2003]] ; [[#ADB--2005|ADB, 2005]] ; [[#Cutter--2008|Cutter and Finch, 2008]] ; [[#World%20Bank--2008|World Bank, 2008]] ; [[#UNISDR--2009|UNISDR, 2009]] ; [[#Crawford--2015|Crawford et al., 2015]] ; [[#Rufat--2015|Rufat et al., 2015]] ; [[#Carrao--2016|Carrao et al., 2016]] ; [[#Gupta--2016|Gupta, 2016]] ; [[#Rahman--2018|Rahman, 2018]] ; [[#Andrijevic--2020|Andrijevic et al., 2020]] ; [[#Jamshed--2020a|Jamshed et al., 2020a]] ; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ; [[#Garschagen--2021|Garschagen et al., 2021]] ). These factors and context conditions also influence individual vulnerability at household or community level. Access to basic services, such as water and sanitation, are linked to human rights and if not granted increase the likelihood that people disproportionately suffer from climate-induced hazards, due to their pre-existing lack of access to such services. In addition, increasing climate hazards further constrain the access to such services ( [[#United%20Nations--2018|United Nations, 2018]] ; [[#Kohlitz--2019|Kohlitz et al., 2019]] ; [[#Gupta--2020|Gupta et al., 2020]] ).

There is an increasing evidence base that successful adaptation and risk reduction strategies need to acknowledge not only climate change and/or specific climate hazards (sea level rise, flooding, droughts, etc.), but also human vulnerability and existing adaptation gaps and thereby the different starting points that societies or different groups have towards climate resilience (see [[#UNEP--2016|UNEP, 2016]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ). Recent reports underscore that development and capacity indicators are useful to assess the broader adaptation challenges and adaptive capacities at global scale independent of a specific climatic hazard. Examples include the percentage of population with access to improved water sources and improved sanitation, the number of physicians per 1000 people or the dependency ratio ( [[#UNEP--2018|UNEP, 2018]] ). These indicators are also part of more comprehensive vulnerability assessments, such as those assessed within this section namely the vulnerability components of the INFORM risk index (e.g., INFORM, 2019) and of the WorldRiskIndex (e.g., [[#Birkmann--2016|Birkmann and Welle, 2016]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ). Recent literature underscores that measuring vulnerability is key for assessing factors that significantly determine actual and future adverse consequences of climate change and complex risks ( [[#Cutter--2008|Cutter and Finch, 2008]] ; [[#Cardona--2012|Cardona et al., 2012]] ; [[#de%20Sherbinin--2019|de Sherbinin et al., 2019]] ; [[#Peters--2019|Peters et al., 2019]] ; [[#Jamshed--2020c|Jamshed et al., 2020c]] ; [[#Visser--2020|Visser et al., 2020]] ; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ). However, there is also important critique on indicator-based assessments of vulnerability (see [[#de%20Sherbinin--2019|de Sherbinin et al., 2019]] ; [[#Rufat--2019|Rufat et al., 2019]] ; [[#Visser--2020|Visser et al., 2020]] ), particularly with regard to issues of validation and its use in decision-making processes. Nevertheless, we observe an emerging agreement in the literature that resilience building and adaptation to climate change has to be informed by climate and multidimensional assessment of the vulnerability of people, different groups and coupled human–environmental systems, including both quantitative and qualitative assessment approaches ( [[#IPCC--2014b|IPCC, 2014b]] ; [[#UNEP--2018|UNEP, 2018]] ; [[#Singleton--2021|Singleton et al., 2021]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ). Since, interdependencies between regional (supranational/sub-continental), national, community and individual vulnerability have often been overlooked, this chapter assesses both global and regional vulnerability, as well as local livelihood vulnerabilities.

While past research regarding the nexus between climate change and poverty often focused on vulnerable groups in rural areas of low-income countries ( [[#de%20Sherbinin--2014|de Sherbinin, 2014]] ; [[#IPCC--2014a|IPCC, 2014a]] ; [[#Barbier--2018|Barbier and Hochard, 2018]] ), new global mega-trends, such as urbanisation, underscore the need to assess both rural and urban communities and their vulnerability. In many rapidly growing cities in the Global South, access to land and to housing is a challenge, particularly for the poor and marginalised, contributing to a further increase in informal settlements that often emerge in highly hazard-exposed areas ( [[#Jeschonnek--2014|Jeschonnek et al., 2014]] ; [[#Rana--2021|Rana et al., 2021]] ). In addition, migration from rural areas to urban centres, also due to increasing adverse impacts of climate change on rural livelihoods, can add another level of complexity ( [[#Flavell--2020|Flavell et al., 2020]] ). Moreover, the context in which such urbanisation processes take place is key. For example, rapidly growing medium-sized cities, for example in West Africa, often do not have sufficient financial, technical and institutional resources to adapt urban structures to climate change ( [[#Birkmann--2016|Birkmann and Welle, 2016]] ; [[#Birkmann--2016|Birkmann et al., 2016]] ; [[#de%20Sherbinin--2017|de Sherbinin et al., 2017]] ). Hence, vulnerability in urban contexts is an emerging issue for international, national and local adaptation programmes. Rather than focusing on mega-cities and their exposure as primary hotspots, more attention has to be given to rapidly growing small- and medium-sized cities and their adaptation needs from the perspective of vulnerability reduction and poverty.

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=== 8.3.2 Global Hotspots of Human Vulnerability to Climate Change ===

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==== 8.3.2.1 Hotspots and Spatial Patterns of Multidimensional Vulnerability ====

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The assessment of literature published since the AR5 suggests that alongside already deteriorated specific conditions that determine individual vulnerability and livelihood security to climate change (see [[#8.2|Section 8.2]] ), high levels of poverty, lack of access to basic services (human rights to water and sanitation), poor governance and conflicts are important factors that characterise vulnerability and systemic human vulnerability in particular (EC-DRMKC, 2020; [[#Wisner--2020|Wisner, 2020]] ; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ; [[#Garschagen--2021|Garschagen et al., 2021]] ; [[#GIZ--2021|GIZ, 2021]] ). These context conditions within a country or region limit the access to effective adaptation options particularly for the poor and marginalised groups.

Recent studies underscore that human vulnerability—thus the predisposition to be adversely affected—is largely determined by past and present development processes, rather than by the occurrence of individual events ( [[#Wisner--2016|Wisner, 2016]] ; [[#Cutter--2018|Cutter, 2018]] ; [[#Birkmann--2020|Birkmann et al., 2020]] ). Also the consequences of the COVID-19 pandemic will create newly poor, particularly in countries that are already characterised by high levels of vulnerability (see Box 8.3; [[#Laborde--2020b|Laborde et al., 2020b]] ; [[#Lakner--2020|Lakner et al., 2020]] ).

Quantitative studies and assessments published since AR5 provide additional insights about human vulnerability to climate change and resilience of societies at different scales using different indicator sets and approaches ( [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ; [[#Hallegatte--2017|Hallegatte et al., 2017]] ; EC-DRMKC, 2020; [[#Birkmann--2021a|Birkmann et al., 2021a]] ; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ; [[#Garschagen--2021|Garschagen et al., 2021]] ).

While quantitative measures of vulnerability are widely used at different scales ( [[#Cutter--2016|Cutter et al., 2016]] ; [[#Garschagen--2021|Garschagen et al., 2021]] ), there are also studies that caution the use of such indices in policy making or risk reduction efforts ( [[#Rufat--2019|Rufat et al., 2019]] ; [[#Spielman--2020|Spielman et al., 2020]] ). Such assessments of vulnerability have to be internally and externally validated and handled with care when applied in decision-making processes in terms of their options and limits. At the same time, these assessments capture important conditions and structures that make people more susceptible to various climate hazards and climate change impacts. The relevance of these conditions is confirmed by quantitative impact assessments as well as many specific case study assessments ( [[#Welle--2015|Welle and Birkmann, 2015]] ; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ). For example, the access to basic services (e.g., water and sanitation) ( [[#Bollin--2013|Bollin and Hidajat, 2013]] ; [[#Pandey--2017b|Pandey et al., 2017b]] ; [[#UNEP--2018|UNEP, 2018]] ; [[#United%20Nations--2018|United Nations, 2018]] ; [[#Gupta--2020|Gupta et al., 2020]] ; [[#Jamshed--2020a|Jamshed et al., 2020a]] ) and broader modes of engagement in governance and governance fragility ( [[#Crawford--2015|Crawford et al., 2015]] ; [[#Rahman--2018|Rahman, 2018]] ; [[#Andrijevic--2020|Andrijevic et al., 2020]] ) significantly influence how climatic hazards translate into severe or non-severe losses and harm (see [[#8.5.2|Section 8.5.2]] ).

The lack of such support structures and resources can severely constrain opportunities of people to cope with and adapt to climate change, since it is not only the climate hazard, but also exposure and particularly the vulnerability of a society, a specific community or an individual household that determine adverse societal consequences of climatic hazards. International vulnerability and resilience assessments show that vulnerability varies across countries of similar wealth or income because multidimensional vulnerability, well-being and resilience depend on a larger set of factors ( [[#Birkmann--2016|Birkmann and Welle, 2016]] ; [[#Hallegatte--2017|Hallegatte et al., 2017]] ; INFORM, 2019). In this regard, vulnerability assessment is significantly different from climate exposure mapping.

The findings of these global assessments suggest, among other issues, that options to reduce vulnerability and enhance resilience do exist in various countries at different levels, in part irrespective of their income level ( [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ; [[#Hallegatte--2017|Hallegatte et al., 2017]] ). Vulnerabilities at national and regional-level influence community and individual vulnerability, particularly through structures that determine entitlements, the access to resources and processes of marginalisation ( [[#Watts--1993|Watts and Bohle, 1993]] ; [[#Thomas--2019|Thomas and Warner, 2019]] ).

While different assessments use different sets of indicators, most of the global assessments with national-scale resolution ( [[#Birkmann--2016|Birkmann and Welle, 2016]] ; [[#Kreft--2016|Kreft et al., 2016]] ; [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ; [[#Hallegatte--2017|Hallegatte et al., 2017]] ; [[#Eckstein--2019|Eckstein et al., 2019]] ; INFORM, 2019; ND-GAIN, 2019; [[#Garschagen--2021|Garschagen et al., 2021]] ), contain indicators that cover different aspects of economic poverty, inequality, access to basic infrastructure services, education and human capital (e.g., adult literacy rate) and some also include issues of gender inequality, specific vulnerable groups or insurance against extreme events. The assessments also differ, for example, in terms of their consideration of aspects of governance, such as corruption and conflict, or the consideration of social safety nets, such as insurance coverage, or the number of people affected by hazards ( [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ; INFORM, 2019), as well as in terms of the consideration of losses experienced in the past or issues such as biodiversity as an aspect of adaptive capacity ( [[#Hallegatte--2017|Hallegatte et al., 2017]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ). Moreover, the assessments differ in terms of the consideration of specific indicators and the inclusion or non-inclusion of specific hazard exposure ( [[#Welle--2015|Welle and Birkmann, 2015]] ; [[#Hallegatte--2017|Hallegatte et al., 2017]] ; INFORM, 2019; ND-GAIN, 2019; [[#Birkmann--2022|Birkmann et al., 2022]] ).

Recent comparative studies of global assessments of vulnerability show ''high agreement'' on the spatial clusters that have very high or very low vulnerability to climate change, compared to larger differences in terms of exposure and risk ( [[#Birkmann--2016|Birkmann and Welle, 2016]] ; [[#Hallegatte--2017|Hallegatte et al., 2017]] ; INFORM, 2019; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ; [[#Garschagen--2021|Garschagen et al., 2021]] ; [[#Schleussner--2021|Schleussner et al., 2021]] ). The comparison of the averaged ranking results at the scale of ‘climate regions’ using the vulnerability components of INFORM and the WorldRiskIndex—as two comprehensive global assessment approaches of systemic vulnerability (hazard independent vulnerability) (see Figures 8.5; 8.6)—also finds a ''high agreement'' in terms of most vulnerable regions and regions with low vulnerability (Figure 8.5; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ). The assessment at this scale reveals that global hotspots of human vulnerability can be found in climate regions in East Africa, Central Africa and West Africa, followed by high vulnerability in Central America, South Asia and Southeast Asia, for example. [[#Garschagen--2021|Garschagen et al. (2021)]] in a comparison of further risk indices also found that there is ''high agreement'' on global assessments of vulnerability compared to exposure or overall risk.

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[[File:63ce1713301d9eb4efacbf36ef8c95c8 IPCC_AR6_WGII_Figure_8_005.png]]

'''Figure 8.5 |''' '''Aggregated vulnerability map at the scale of climate regions based on the averaged ranking of the INFORM Index’s vulnerability component and the averaged ranking of the vulnerability component of the WorldRiskIndex.''' Based on the rankings of the INFORM index (INFORM, 2019) and the WorldRiskIndex ( [[#Birkmann--2016|Birkmann and Welle, 2016]] ; [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ). The map and diagram show agreement between the two global vulnerability indices when ranking climate regions according to their vulnerability—darker colours show regions of higher vulnerability. The diagram shows how the 35 climate regions are ranked by each index and also serves as a legend for the map above.

The analysis of vulnerability assessment results of the INFORM Risk Index and WorldRiskIndex [[#footnote-002|4]] at the level of countries coupled with population data confirms a ''high agreement'' on most vulnerable countries. It also shows that global hotspots of human vulnerability are not just single countries, but often emerge within regional clusters, particularly in Africa, but also in Asia and Central America (see Figure 8.6 and [[#Birkmann--2021a|Birkmann et al., 2021a]] ). These regional clusters (Figure 8.6) are characterised by high levels of vulnerability in terms of socioeconomic, demographic, environmental and governance conditions that make people more likely to face adverse consequences once a climate hazard occurs. The internal and external validation of these index systems shows its statistical validity and robustness ( [[#Welle--2015|Welle and Birkmann, 2015]] ; [[#Marin-Ferrer--2017|Marin-Ferrer et al., 2017]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ). It also confirms a quantitative relationship between most vulnerable regions and fatalities and severely affected people due to climate-influenced hazards ( [[#Birkmann--2022|Birkmann et al., 2022]] ). The vulnerability map in Figure 8.5 shows the vulnerability level (systemic societal vulnerability) linked to national scale and provides additional information about the population density within these countries. The background map does not show specific vulnerable populations within countries. Selected examples of sub-national human vulnerabilities have been added as additional information in terms of case studies based on information from other chapters within this report (see, for example, Box 8.7; Sections 5.12; 10.3.3; 10.5.1; 13.8.1; 14.4.7; 15.3.4; Cross-Chapter Paper 6.2.7).

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[[File:3dde51887ab6ef849965df6708b7cd62 IPCC_AR6_WGII_Figure_8_006.png]]

'''Figure 8.6 |''' '''Global map of vulnerability.''' This map shows the relative level of average national vulnerability as calculated by global indices (INFORM and WRI see details in 8.3.2). Areas shaded light yellow are on average the least vulnerable and those shaded darker red are the most vulnerable. The map combines information about the level of vulnerability (independent of the population size) with the population density (see legend) to show where both high vulnerability and high population density coincide. The map reveals that there are densely populated areas of the world that are highly vulnerable, but also highly vulnerable populations in more sparsely populated areas. There are also highly vulnerable communities and populations in countries with overall low vulnerability as shown with local case studies alongside the map. The pie charts show the number of deaths (mortality) per hazard (storm, flood, drought, heatwaves and wildfires) event per continental region based on EM-DAT Data ( [[#CRED--2020|CRED, 2020]] ). The size of the pie chart represents the average mortality per hazard event while slices of each pie chart show the absolute number of deaths from each hazard. This reveals that over the past decade, there were significantly more fatalities per hazard in the more vulnerable regions, e.g., Africa and Asia. The analysis of the data shown in this map revealed that over 3.3 billion people are living in countries classified as very highly and highly vulnerable, while approximately 1.8 billion people live in countries with low and very low vulnerability ( [[#Birkmann--2022|Birkmann et al., 2022]] ). These vulnerability values are based on the average of the vulnerability components of the INFORM Index (INFORM, 2019) and WorldRiskIndex ( [[#Birkmann--2016|Birkmann and Welle, 2016]] ; [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ) with updated data from 2019 classified into five classes using the quantile method. Other studies applied more vulnerability classes within their assessment and therefore provide slightly different numbers ( [[#Birkmann--2021a|Birkmann et al., 2021a]] ). However, despite different calculation methods, the conclusion remains that there are significantly more people residing in countries with very high and highly vulnerability compared to those living in countries classified as having low or very low vulnerability.

Figure 8.7 provides an aggregated regional overview of selected indicators used within the vulnerability index mapped in Figure 8.6. The overview shows that the many compounded challenges faced by African countries are starkly pronounced, but also in other regions, especially Asia, Central and South America, and among SIDS, there are several challenges such as inequality, governance issues and displacement, which all increase the vulnerability and constrain adaptive capacities of these regions to climate change.

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[[File:bfe101cab66e8d02fc1fcdd6f9acc94b IPCC_AR6_WGII_Figure_8_007.png]]

'''Figure 8.7 |''' '''The figure shows selected aspects of human vulnerability, such as extreme poverty and inequality, and access to health care and basic infrastructure as regional averages.''' These vulnerability aspects are a selection of indicators from the indicator systems (the INFORM Risk Index and WorldRiskIndex 2019) used for the global vulnerability map (Figure 8.6). These normalized indicator scores were averaged for each region and classified into three levels of severity using the natural breaks method. This figure provides a more differentiated picture about the various dimensions of vulnerability that different regions and countries face and the severity of such challenges in each region. Such vulnerability challenges increase the risk of severe adverse impacts of climate change and related hazards ( [[#Birkmann--2022|Birkmann et al., 2022]] ).

However, it is also important to note that vulnerability assessments do have their limitations ( [[#Heesen--2014|Heesen et al., 2014]] ; [[#Rufat--2019|Rufat et al., 2019]] ). For example, in high-income countries, specific groups can be highly vulnerable to climate change due to marginalisation and discrimination due to ethnicity or gender. Gender inequality, for example, is also high in some countries classified in the literature as having low vulnerability (see [[#Birkmann--2021a|Birkmann et al., 2021a]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ). Nevertheless, these countries have, in theory, sufficient financial resources and governance capacities to deal with these challenges, while this is different for many country clusters classified as highly vulnerable.

Countries and regional clusters with low vulnerability (see Figures 8.5; Figure 8.6), such as Australia and New Zealand or Iceland and North Europe, encompass population groups that are exposed and vulnerable to climate hazards, such as sea level rise or droughts but, within these regions’ context, conditions exist that allow the negative impacts and losses to be buffered (also for most vulnerable groups). These regions have higher financial and institutional capacities to support people at risk and planned adaptation at a different magnitude within their region, for example, as seen in compensation payments for drought exposed farmers ( [[#Hochrainer-Stigler--2017|Hochrainer-Stigler and Hanger-Kopp, 2017]] ; Australian-Government, 2021) or flood affected households in Germany in 2021. Also, the percentage of households insured against climate-influenced hazards, such as floods or storms, is significantly higher in these regions (North America, Western Europe) compared to regions such as Western Africa or Micronesia ( [[#Welle--2015|Welle and Birkmann, 2015]] ; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ).

While climate change differentially impacts people in vulnerable situations within countries, including the poor, children, women, marginalised Indigenous or other ethnic minority people ( [[#Rhiney--2016|Rhiney et al., 2016]] ; [[#Méndez--2020|Méndez et al., 2020]] ), the global assessment results underscore that, in most vulnerable regions and countries, very limited resources and structures exist to support these groups when droughts, floods or storms occur and place an additional burden on these groups.

The assessments of human vulnerability also point towards important adaptation options that are not visible if one focuses on climatic hazards or temperature changes alone (Figure 8.9; [[#Dückers--2015|Dückers et al., 2015]] ; [[#Cutter--2016|Cutter et al., 2016]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ). Fundamental for vulnerability reduction and adaptation are social insurances and infrastructure programmes, as well as legislation that improves the access of poor and marginalised groups to basic infrastructure services and security. For example, the ‘free basic service programme’ of the national government of South Africa (GovSA, 2021) is one example where a national government (Government of South Africa) has committed itself to providing a basic amount of free water, electricity and sanitation to low-income households, particularly indigent people, such as those living in informal settlements or remote rural areas. Coupled with incentives, for example in terms of a higher use of renewable energy (e.g., solar home systems in rural areas) (see GovSA, 2021), these investments can support vulnerability reduction and mitigation of GHG emissions. However, the programme design and implementation has also been criticised (see [[#Nel--2005|Nel and Rogerson, 2005]] ; [[#Muller--2008|Muller, 2008]] ), as is witnessed by ongoing service delivery protests ( [[#Mutyambizi--2020|Mutyambizi et al., 2020]] ). This example shows that current national programmes can—even if they are not classified as adaptation measures—provide important entry points to reduce human vulnerability to climate change.

The relevance of human vulnerability has also been confirmed by recent assessments. The assessment of vulnerability studies and mortality data found that the average mortality [[#footnote-001|5]] from floods, storms and droughts is 15 times higher in regions and countries ranked as very highly vulnerable (e.g., Afghanistan, Haiti, Mozambique, Nigeria, Somalia) compared to regions and countries with very low vulnerability (e.g., Canada, Italy, Sweden, UK) ( [[#Birkmann--2022|Birkmann et al., 2022]] ). These patterns are confirmed by other studies (e.g., [[#CRED%20and%20UNDRR--2015|CRED and UNDRR, 2015]] ; [[#CRED%20and%20UNDRR--2016|CRED and UNDRR, 2016]] ; [[#CRED%20and%20UNDRR--2020b|CRED and UNDRR, 2020b]] ) that examined disaster mortality per hazard event in low and lower middle income countries compared to high income countries and therewith also point towards major differences between countries with high and low vulnerability ( [[#Pelling--2004|Pelling et al., 2004]] ; [[#CRED%20and%20UNDRR--2015|CRED and UNDRR, 2015]] ; [[#CRED%20and%20UNDRR--2016|CRED and UNDRR, 2016]] ; [[#CRED%20and%20UNDRR--2020b|CRED and UNDRR, 2020b]] ). Even if one takes solely ‘highly vulnerable countries’ such as India, Pakistan and the Philippines (and not ‘very highly’ vulnerable countries), mortality is still nine times higher compared to very low vulnerability countries. Similarly, studies further revealed that average number of adversely affected people per hazard event (e.g., loss of the house) is 11 times higher in regions and countries categorised as having very high vulnerability compared to very low vulnerability ( [[#Birkmann--2022|Birkmann et al., 2022]] ). In addition to floods, droughts and storms, published EM-DAT data for wildfires and heat stress, confirmed higher suffering (higher average mortality) in more vulnerable regions compared to less vulnerable regions, particularly when excluding extreme outliers ( [[#CRED--2020|CRED, 2020]] ). These findings point towards the fact that in regions identified as highly vulnerable in the assessments even moderate future climate change and future climate hazards are likely to push people further into poverty and lead to significant destabilisation processes in terms of livelihoods security ( [[#Wallemacq--2018|Wallemacq and House, 2018]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ).

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===== 8.3.2.1.1 Historic roots of vulnerability in regions classified as highly vulnerable =====

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While increasing attention is given to issues of human vulnerability, less attention has been given to the historical conditions that foster systemic vulnerability of societies. It is important to acknowledge that drivers and root causes of systemic human vulnerabilities and development challenges are not always new, and sometimes—for example in various countries in Africa, Asia and the Caribbean—can be linked to histories of imperialism, colonial structures ( [[#Grasham--2019|Grasham et al., 2019]] ), and subsequent development and governance contexts ( [[#Southard--2017|Southard, 2017]] ; [[#Zhukova--2020|Zhukova, 2020]] ). Thus, root causes of present structures of human and human–environmental vulnerability often have historic dimensions, for example, chronic poverty and structural inequality in Africa ( [[#Grasham--2019|Grasham et al., 2019]] ) or the Caribbean are still influenced by the colonial power relations outside of these countries making solutions for vulnerability reduction more difficult (see e.g., [[#Douglass--2020|Douglass and Cooper, 2020]] ). In addition, national borders, such as in many regions in Africa, sometimes cut through ethnic groups and therewith ignore important interrelations between communities on both sides of the border.

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===== 8.3.2.1.2 People residing in most vulnerable versus least vulnerable regions =====

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While global assessments often allow for country rankings, it is similarly important to better understand how many people are living in these different levels of vulnerability. The quantitative assessments underscore that a significantly higher number of people live in countries with very high and high vulnerability compared to the population living in countries classified as having low and very low vulnerability. An analysis that measured the vulnerability of countries according to the INFORM Risk Index and the WorldRiskIndex vulnerability index components, differentiating vulnerability values into seven vulnerability classes found that nearly twice as many people are living in most vulnerable countries compared to the number living in less vulnerable countries ( [[#Birkmann--2021a|Birkmann et al., 2021a]] ). Another study that uses the same data and differentiates vulnerability into five classes (also considering the lack of coping capacity within the INFORM index, see ( [[#Marin-Ferrer--2017|Marin-Ferrer et al., 2017]] )) concludes that about 3.3 billion people are living in countries classified as highly vulnerable, while approximately 1.8 billion people live in countries with low vulnerability ( [[#Birkmann--2022|Birkmann et al., 2022]] ). Additional assessments based on the classification of income groups of countries reveal that approximately 3.6 billion people live in low and lower middle-income countries, which are most vulnerable and disproportionally bear the human costs of disasters due to extreme weather events and hazards (World Bank, 2019b; [[#CRED%20and%20UNDRR--2020b|CRED and UNDRR, 2020b]] ; EC-DRMKC, 2020; [[#UN-DESA--2020a|UN-DESA, 2020a]] ; [[#UN-DESA--2021|UN-DESA, 2021]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ). While these numbers are different, both results underscore that the absolute and relative number of people living in most vulnerable contexts is significantly higher compared to those that live in a country with a low vulnerability status ( [[#Birkmann--2021a|Birkmann et al., 2021a]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ). These differences have also been observed in former years ( [[#Welle--2015|Welle and Birkmann, 2015]] ; [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ).

That means, even moderate changes in the global mean temperature, as identified in the recent IPCC report SR1.5°C ( [[#IPCC--2018c|IPCC, 2018c]] ) and in scientific literature ( [[#Hoegh-Guldberg--2019a|Hoegh-Guldberg et al., 2019a]] ), can mean substantial increases in risks for more than 3 billion people due to high levels of vulnerability.

Overall, there is ''robust evidence'' and ''high agreement'' in the recent literature that countries and regions classified as highly vulnerable face multiple development challenges at once, in which high levels of poverty interact with limited access to water and sanitation or with high levels of forced migration and, in some cases, with state fragility making solutions difficult ( [[#Hallegatte--2017|Hallegatte et al., 2017]] ; [[#Marin-Ferrer--2017|Marin-Ferrer et al., 2017]] ; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ; [[#Garschagen--2021|Garschagen et al., 2021]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ). High levels of vulnerability within these regional clusters are the product of current development challenges, but are often caused by long and complex histories, including issues of colonisation and marginalisation, for example, in hotspots in Africa ( [[#Birkmann--2021a|Birkmann et al., 2021a]] ).

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<span id="transboundary-vulnerability-and-adaptation"></span>
==== 8.3.2.2 Transboundary Vulnerability and Adaptation ====

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Next to the identification of the level of agreement between different vulnerability assessments ( [[#Garschagen--2021|Garschagen et al., 2021]] ) and the spatial hotspots, global assessments of vulnerability and adaptation readiness also point towards the need for a transboundary perspective and transboundary cooperation in terms of vulnerability reduction and adaptation ( [[#Tilleard--2016|Tilleard and Ford, 2016]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ). Newer research points towards the fact that various phenomena of vulnerability, particularly in highly vulnerable regions, spill over national borders and emerge in rather regional clusters, such as forced migration and poverty in West and Central Africa, as well as conflicts in the Near East and Asia ( [[#IDMC--2020|IDMC, 2020]] ). This means that regional and transboundary challenges contribute to the formation of systemic human vulnerability, for example, forced migration that is occurring within countries, but also across international borders that is also influenced by climate change ( [[#Kaczan--2020|Kaczan and Orgill-Meyer, 2020]] ). In summary, these findings point towards the need for more transboundary approaches in vulnerability and risk reduction, adaptation and development. Recent literature and data presented in Figure 8.6 and ( [[#Birkmann--2016|Birkmann and Welle, 2016]] ; [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ; [[#Hallegatte--2017|Hallegatte et al., 2017]] ; INFORM, 2019; [[#Birkmann--2021a|Birkmann et al., 2021a]] ) demonstrate the need to strengthen approaches to monitor the regional dimensions of vulnerability and to develop strategies and programmes that consider transboundary vulnerability in risk reduction and cooperation at different scales. This includes, for example, cooperation between national-level institutions, but also transboundary networks of cities or communities ( [[#Tilleard--2016|Tilleard and Ford, 2016]] ; [[#Benzie--2019|Benzie and Persson, 2019]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ). The transnational nature of climate change impacts means that addressing them requires concerted efforts among nations ( [[#IPCC--2014b|IPCC, 2014b]] ; [[#Dzebo--2019|Dzebo, 2019]] ).

In addition, national response strategies for specific transboundary climate-influenced hazards, such as river flooding, droughts or coastal flooding can also significantly influence neighbouring countries and can affect exposure and vulnerability of the respective country ( [[#Nadin--2018|Nadin and Roberts, 2018]] ; [[#Booth--2020|Booth et al., 2020]] ). Likewise, climate change may affect transboundary resources (e.g., underground water reserves) and transboundary ecosystems (e.g., in terms of the migration of species) ( [[#Vij--2017|Vij et al., 2017]] ) and thereby further reduce the capacity of vulnerable groups to cope and adapt. In addition, recent research indicates that social inequities are also coupled with access to and quality of environmental resources in urban environments—meaning social and environmental justices are interconnected (see [[#Schell--2020|Schell et al., 2020]] ).

Individual adaptation projects to specific climate hazards in regions classified as highly vulnerable are needed. However, recent studies underscore that deeper development challenges need to be addressed in order to make progress towards adaptation and vulnerability reduction and to avoid maladaptation ( [[#Eriksen--2021|Eriksen et al., 2021]] ). Adaptation and development projects, such as the construction of a dam as a response to water shortages in one country can significantly influence the exposure to water shortages and the response capacities of another country downstream. Often, transboundary challenges are a result of policy and resource management choices or uncertainty, and addressing them requires a greater engagement between governing bodies, which may also guide more suitable responses in the context of climate change adaptation and vulnerability reduction ( [[#Earle--2015|Earle et al., 2015]] ; [[#Tilleard--2016|Tilleard and Ford, 2016]] ; [[#McLeman--2018|McLeman, 2018]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ).

Most of those countries and regional clusters identified as highly vulnerable have contributed little to the overall amount of GHG emissions and therefore support for (transboundary) adaptation from the international community is required in these places and for those living under these conditions in order to support and achieve climate justice.

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<span id="the-effect-of-higher-levels-of-global-warming-for-most-vulnerable-regions-and-specific-livelihoods"></span>
==== 8.3.2.3 The Effect of Higher Levels of Global Warming for Most Vulnerable Regions and Specific Livelihoods ====

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Evidence exists that threats to land-based livelihoods and risks of undernutrition increase significantly with higher levels of global warming ( [[#Hoegh-Guldberg--2019a|Hoegh-Guldberg et al., 2019a]] ). With global warming of 1.5°C or less, impacts of climate change on livelihoods are still significant, for example, for West Africa and the Sahel there will be an estimated reduction of the area suitable for maize production of about 40%. The consequences of global warming of up to 3°C would mean a high risk of undernutrition for entire regions (see [[#Hoegh-Guldberg--2019a|Hoegh-Guldberg et al., 2019a]] ) that are already classified as most vulnerable (see Figure 8.6). That means the consequences of significant warming are a particular challenge for regional hotspots of vulnerability. Small changes in crop productivity, already observed due to increasing droughts, floods or changes in rainfall patterns, could lead to severe health risks and undernutrition. This is because of existing precarious living conditions and the limited capacities that people and institutions have to build and enhance coping and adaptive capacities at the level of individual households, communities and state institutions (see [[#UNEP--2018|UNEP, 2018]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ). The risk of loss of life, displacement and adverse health consequences due to climate change in these most vulnerable regions (such as Micronesia, South Asia, West Africa—see Figures 8.5; 8.6) is higher compared to regions classified as having medium or low vulnerability ( [[#Birkmann--2022|Birkmann et al., 2022]] ). Nevertheless, other regions and countries classified as less vulnerable, for example in Asia, are experiencing disasters and have a relative high share of the observed global fatalities or losses, when considering non-climatic natural hazards ( [[#CRED%20and%20UNDRR--2020a|CRED and UNDRR, 2020a]] ; see also [[#8.3.2.1|Section 8.3.2.1]] ). In addition, changing climatic hazard and exposure patterns have to be considered. However, the agreement of major global index systems on exposure is significantly lower compared to vulnerability ( [[#Garschagen--2021|Garschagen et al., 2021]] ).

Moreover, the assessment reveals that in most vulnerable regions a double burden of existing destabilised livelihood conditions and additional climatic hazards is already visible and largely influences societal impacts of climate change. For example, flooding along the White Nile in Uganda and South Sudan hit vulnerable communities that were displaced due to conflicts and were thus uprooted again by flooding ( [[#IDMC--2020|IDMC, 2020]] ). Societal impacts and future risks of climate change to societies need to incorporate information about vulnerability and exposure—including capacities of people to cope and adapt ( [[#Wisner--2016|Wisner, 2016]] ; Cardona et al., 2020). There is increasing evidence that individual and societal capacities to cope and adapt also depend on how governmental and national institutions can support people at risk (see [[#8.6|Section 8.6]] ). For example, climate information services depend on a functioning weather service. Likewise, social safety nets as an adaptation strategy require financial resources, which are often absent for most people in highly vulnerable regions. In addition, examples of national programmes that target most vulnerable groups, such as the free basic service programme in South Africa, show that next to the adaptation to individual hazards, strategies exist that aim to reduce systemic human vulnerability (see GovSA, 2021).

At the same time, there is scientific evidence that more intense and frequent climate-influenced hazards (e.g., storms, flooding, droughts, heat stress) can undermine decade-long poverty reduction efforts, particularly in most vulnerable regions ( [[#Mysiak--2016|Mysiak et al., 2016]] ; [[#Formetta--2019|Formetta and Feyen, 2019]] ; [[#Laborde--2020b|Laborde et al., 2020b]] ; [[#Lakner--2020|Lakner et al., 2020]] ). There is ''high agreement'' that, with global warming of about 3°C, such undermining of poverty reduction efforts will intensify and more regions will face development setbacks due to the spatial and temporal expansion of climate hazards, including the further erosion of capital that enables people to develop adaptive capacities ( ''high confidence'' ) (see [[#8.5|Section 8.5]] ). Such trends can further exacerbate poverty traps (see [[#8.2|Section 8.2.2]] ). According to a World Bank report, between 32 and 132 million people could fall into extreme poverty by 2030 due to the impacts of climate change ( [[#Jafino--2020|Jafino et al., 2020]] ). Models estimate that at 3°C warming and under Shared Socioeconomic Pathway (SSP) 1, there would be an additional 245 million people exposed to poverty. Under SSP2 this number would increase to 904 million additional people exposed to poverty (SSP2) and under SSP3 (with significant challenges for equity) about 1918 million additional people could be exposed to poverty in the year 2050 ( [[#Byers--2018|Byers et al., 2018]] ).

Overall, the assessments above underscore that adaptation and risk reduction require not only information about changing climatic conditions, but also assessments that capture the development contexts and structural inequality that determine and influence human vulnerability. Strategies that reduce poverty and inequality and that improve the access of people to basic services need to become a higher priority in adaptation and development planning in order to avoid more than 3 billion people currently and even more in the future being exposed to severe adverse consequences of climate change. Reducing vulnerability to climate change is therefore indispensable for climate justice and just transitions ( ''high confidence'' ).

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<span id="compound-challenges-vulnerability-and-state-fragility"></span>
==== 8.3.2.4 Compound Challenges: Vulnerability and State Fragility ====

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Literature in the area of climate change risk management and adaptation highlights the importance of overall governance systems and their functioning and inclusiveness in terms of vulnerability and risk reduction ( [[#Burch--2019|Burch et al., 2019]] ). Empirical evidence and scientific studies show linkages between issues of governance, conflicts and high levels of state fragility and systemic human vulnerability (see Figure 8.8; [[#8.5.2|Section 8.5.2]] ; [[#Eklöw--2019|Eklöw and Krampe, 2019]] ; [[#Peters--2019|Peters et al., 2019]] ; [[#Mawejje--2020|Mawejje and Finn, 2020]] )

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[[File:84ace6fe482c11ce5c7266c2d11c4834 IPCC_AR6_WGII_Figure_8_008.png]]

'''Figure 8.8 |''' '''Comparison of the vulnerability and state fragility of global regions.''' The vulnerability values are the average of the vulnerability component of the WorldRiskIndex 2019 ( [[#Birkmann--2021a|Birkmann et al., 2021a]] ; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ) and the vulnerability and lack of coping capacity components of the INFORM Risk Index 2019 ( [[#Marin-Ferrer--2017|Marin-Ferrer et al., 2017]] ) classified into five classes using the equal count method ( [[#Birkmann--2022|Birkmann et al., 2022]] ). The state fragility values are based on the Fragile States Index 2019 ( [[#FFP--2020|FFP, 2020]] ) and regions are based on the intermediate and sub-regions of the United Nations Statistical Division. The size of each circle is proportional to the population (World Bank, 2019b) in the respective region.

The comparison of state fragility and vulnerability at the level of regions (United Nations Statistics Division regions) based on the vulnerability information of the INFORM and WorldRiskIndex systems and information from the Failed State Index indicates clear linkages (see Figure 8.8), meaning that societal development and governance challenges often interact and, in many cases, are influenced by complex histories (see [[#FFP--2020|FFP, 2020]] ; [[#Birkmann--2021a|Birkmann et al., 2021a]] ; [[#Feldmeyer--2021|Feldmeyer et al., 2021]] ). Strategies to reduce systemic vulnerability and multidimensional poverty have to account for these broader governance challenges that hamper resilience building and the development of adaptive capacities to climate change at various levels.

Strategies to strengthen adaptation to climate change have therefore to acknowledge these interdependencies between climate change, vulnerability, development and governance (see [[#8.6|Section 8.6.5]] ). The results of different global vulnerability assessments and the role of governance conditions underscore that next to individual adaptation projects in specific sectors, integrated strategies and programmes are needed that reduce systemic vulnerability and support enabling conditions for adaptation for most vulnerable groups (see [[#8.6|Section 8.6.5]] ).

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<span id="trends-in-vulnerability-and-poverty-in-light-of-climate-change-and-the-covid-19-pandemic"></span>
==== 8.3.2.5 Trends in Vulnerability and Poverty in Light of Climate Change and the COVID-19 Pandemic ====

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Literature that assesses trends of poverty and vulnerability, as well as exposure to climate change, reveals that geographic patterns of poverty and vulnerability are uneven and changing over time ( [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ). However, a robust finding of different studies is that population growth in most vulnerable country groups and regions ‘is’ and ‘will be’ significantly higher in the future compared to population growth in countries classified as having low vulnerability (see [[#8.4.5.2|Section 8.4.5.2]] ). In summary, a significant increase of population is expected in highly vulnerable countries in the future. In addition, global studies predict that, by 2030, almost 50% of the world’s poor will be living in countries affected by state fragility, conflict and violence ( [[#UNISDR--2009|UNISDR, 2009]] ; [[#Hallegatte--2017|Hallegatte et al., 2017]] ).

Another important phenomenon that modifies trends in vulnerability to climate change and poverty is the COVID-19 pandemic (see Box 8.3). It is ''likely'' that the COVID-19 pandemic with its global repercussions will continue to modify and, in many cases, intensify poverty and human vulnerability ( [[#Laborde--2020a|Laborde et al., 2020a]] ; [[#Sumner--2020|Sumner et al., 2020]] ). Recent studies that estimate the impact of COVID-19 on global poverty agree that a significant increase of poverty due to COVID-19 and the respective lockdown of countries is already observed or expected in the near future ( [[#Laborde--2020b|Laborde et al., 2020b]] ; [[#Sumner--2020|Sumner et al., 2020]] ). These studies underscore that 80% of those newly living in extreme poverty (living on under 1.9 USD d −1 ) due to COVID-19 would be mainly located in two global regions: sub-Saharan Africa and South Asia ( [[#Sumner--2020|Sumner et al., 2020]] ). Consequently, the COVID-19 pandemic is ''likely'' to further increase inequality at different scales and increase the burden within regions already characterised by a significant adaptation gap in terms of high vulnerability (see also Figure 8.6). This implies that the capacity of people to prepare for present and future climate change impacts will further decrease within these countries and for specific vulnerable people or groups in these regions.

Recent scientific studies in the context of climate-influenced hazards and disasters also underscore that various regions and countries classified as highly vulnerable are characterised by a high persistence of human vulnerability and chronic poverty ( [[#Feldmeyer--2017|Feldmeyer et al., 2017]] ; [[#UN-DESA--2020b|UN-DESA, 2020b]] ; [[#World%20Bank--2020|World Bank, 2020]] ). For example, various highly vulnerable regions in Central, West and East Africa, countries such as Afghanistan, Democratic Republic of Congo and Haiti, and also SIDS in Melanesia and Micronesia have been characterised by high levels of poverty for decades ( [[#World%20Bank--2020|World Bank, 2020]] ). Several of these highly vulnerable regions are also ''likely'' to experience a further increase in climate hazards such as sea level rise in Melanesia and Micronesia and in coastal zones of West Africa and more severe droughts in Africa ( [[#IPCC--2021|IPCC, 2021]] ).

There is ''robust evidence'' that in many world regions the exposure to climatic hazards is increasing with additional global warming ( [[#Chin-Yee--2019|Chin-Yee, 2019]] ; [[#Hoegh-Guldberg--2019a|Hoegh-Guldberg et al., 2019a]] ; [[#IPCC--2021|IPCC, 2021]] ). In addition, development patterns and practices such as urbanisation and migration to exposed areas, for example, to coastal zones in West Africa or South Asia is increasing exposure. While the spatial and temporal exposure to impacts from climate change and extreme events increases with higher levels of global warming ( [[#Hoegh-Guldberg--2019a|Hoegh-Guldberg et al., 2019a]] ), in all global regions and various climate zones ( [[#IPCC--2021|IPCC, 2021]] ), the burden is greater for the most vulnerable regions where people have limited support and capacities to build adaptive capacities for future impacts of climate change.

In this regard, vulnerability assessment results provide an important additional layer of information for decision making in terms of defining adaptation and risk reduction needs and priorities, as shown in Figure 8.9. The figure shows the published climatic information regarding observed changes in agricultural and ecological droughts ( [[#IPCC--2021|IPCC, 2021]] ) combined with a background map of vulnerability. For example, the combined information reveals that even if the agreement on the type of changes observed in droughts is low for North and southeast Africa, it is the high vulnerability in this region that requires urgent attention (see Figure 8.9).

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[[File:d35a1a467bfe34c59d3637df6676ce1d IPCC_AR6_WGII_Figure_8_009.png]]

'''Figure 8.9 |''' '''Map with observed changes in agricultural and ecological droughts''' '''( [[#IPCC--2021|IPCC, 2021]] ) overlaid over human vulnerability (see Figure 8.''' '''6) provides a more comprehensive overview for defining adaptation priorities.'''

Recent reports on extreme poverty and human rights ( [[#Alston--2019|Alston, 2019]] ) show that millions already face malnutrition due to devastating drought. In addition, the linkages between ecosystem vulnerability and human vulnerability and human well-being are important aspects that need more attention, since, for example, the degradation of marine ecosystems that support food systems for hundreds of millions of people will threaten food security (see for details Cross-Chapter Box MOVING PLATE in Chapter 5).

While the findings of the Alston report underscore the urgency to act in order to protect people’s livelihoods, particularly in low-income countries, it also shows that extreme poverty ( [[#Alston--2019|Alston, 2019]] ) and different dimensions of poverty are found in middle- and high-income countries.

A study of the World Bank ( [[#Hallegatte--2017|Hallegatte et al., 2017]] ) estimates that losses in terms of well-being are significantly higher than actual asset losses experienced ( [[#Hallegatte--2017|Hallegatte et al., 2017]] ). A higher proportion of the global absolute economic losses occurred in high-income countries. About 56% of all disasters reported occurred in high-income countries, while the low-income countries account for 44% of the recorded disasters. However, low-income countries account for about 68% of the total deaths reported, high-income countries for about 32% ( [[#CRED%20and%20UNDRR--2020b|CRED and UNDRR, 2020b]] ). In contrast, average absolute economic losses [[#footnote-000|6]] were significantly lower in most vulnerable countries compared to low vulnerable countries ( [[#Birkmann--2022|Birkmann et al., 2022]] ). Economic loss trends from EM-DAT database ( [[#CRED--2020|CRED, 2020]] ) must be interpreted with caution. Economic loss data is often incomplete and needs to be improved. However, these differences in terms of economic losses can also be explained in part by the significant wealth differences and the monetary value of assets exposed. Consequently, there is a need to critically reflect on the measures used to assess L&amp;D from climate change. Interestingly, the number of people affected by droughts, floods and storms as a percentage of the total population and per hazard event again points to the disproportionate suffering of most vulnerable countries ( [[#Birkmann--2022|Birkmann et al., 2022]] ).

Overall, there is ''robust evidence'' that at the global scale poor and most vulnerable people, particularly in regions classified as highly vulnerable, are disproportionately affected by well-being losses and loss of life in the context of climate change and climate-influenced natural hazards ( [[#CRED%20and%20UNDRR--2015|CRED and UNDRR, 2015]] ; [[#Hallegatte--2017|Hallegatte et al., 2017]] ; [[#Birkmann--2022|Birkmann et al., 2022]] ) ( ''high agreement'' ). In this context, non-economic losses also need to receive more attention (see [[#8.3.3.2|Section 8.3.3.2]] ). While there is an emerging understanding that inequality and multidimensional poverty are important determinants of systemic vulnerability to climate change ( [[#Dennig--2015|Dennig et al., 2015]] ; [[#Hallegatte--2017|Hallegatte and Rozenberg, 2017]] ; [[#Islam--2017|Islam and Winkel, 2017]] ) that affects more than 3 billion people today, only very few countries explicitly aim to reduce poverty and income inequality as an adaptation measure (see e.g., [[#Brazil%20Ministry%20of%20Environment--2016|Brazil Ministry of Environment, 2016]] ) ''(high agreement)'' . Reducing vulnerability is a prerequisite for climate justice and just transitions.

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<span id="livelihood-impacts-shifting-livelihoods-and-the-challenges-for-equity-and-sustainability-in-the-context-of-climate-change"></span>
=== 8.3.3 Livelihood Impacts, Shifting Livelihoods and the Challenges for Equity and Sustainability in the Context of Climate Change ===

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This section complements the global and regional assessment of vulnerability in the previous section with a more precise assessment of observed local conditions and livelihood impacts and shifts. First, the section reviews linkages between vulnerability and livelihood impacts of climate change broadly. Second, it examines the range of observed disproportionate impacts according to economic (e.g., income) and non-economic (e.g., cultural) impacts of climate change. Third, it examines current risks of adaptation limits and compounding effects across social groups and associated livelihood shifts.

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<span id="the-implications-of-vulnerability-for-past-and-present-livelihood-impacts-of-climate-change"></span>
==== 8.3.3.1 The Implications of Vulnerability for Past and Present Livelihood Impacts of Climate Change ====

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Climate change impacts add to livelihood challenges and can further increase inequality and poverty (see [[#8.2.1|Section 8.2.1]] ), whose root causes are social, institutional and governance related. Various regional clusters of high vulnerability (see Figure 8.6) are also influenced by historical processes, such as colonialism and power relations that made people and countries vulnerable ( [[#Schell--2020|Schell et al., 2020]] ). Thus, vulnerability to climate change is not primarily linked to the degree of exposure to climate change impacts, but determined by societal structures and development processes that shape context and individual vulnerability (see [[#8.3.2|Section 8.3.2]] ), and values and lived experiences of climate hazards ( [[#Djoudi--2016|Djoudi et al., 2016]] ; [[#Walker--2021|Walker et al., 2021]] ). Intersectionality approaches are central to grasping differential vulnerability ( [[#Thomas--2019|Thomas et al., 2019]] ) for past and present livelihood impacts of climate change (see Figure 8.3; [[#8.2.2.2|Section 8.2.2.2]] ). Assessing observed local conditions and livelihood impacts and shifts requires us to consider reinforcing social phenomena such as age, gender, class, race and ethnicity, which shape social inequalities and experiences of the world and also intersect with climate hazards and vulnerability ( [[#Walker--2021|Walker et al., 2021]] ).

This understanding helps to clarify how social structures, institutions and governance mechanisms matter to address social causes in addition to climate magnifiers while holding them accountable (see [[#8.5|Section 8.5]] ). For example, low-elevation coastal zones concentrate high levels of poverty in some specific areas: 90% of the world’s rural poor are concentrated in the low-elevation coastal zones of just 15 countries, and this population keeps growing ( [[#Barbier--2015|Barbier, 2015]] ). Yet studies on the economic impacts of climate change and also integrated assessment models typically overlook the distributional effects of these impacts according to vulnerability and exposure and do not sufficiently account for agent and societal heterogeneity ( [[#Balint--2017|Balint et al., 2017]] ; [[#Sovacool--2021|Sovacool et al., 2021]] ).

Since the AR5, ''high confidence'' is attributed to the fact that the, mostly detrimental, climate change impacts and risks are experienced mainly by the poorest people around the world ( [[#Olsson--2014|Olsson et al., 2014]] ; [[#Roy--2018|Roy et al., 2018]] ). There is ''high confidence'' that climate change impacts will put a disproportionate burden on low-income households and thus increase poverty levels ( [[#IPCC--2014a|IPCC, 2014a]] ; [[#Hallegatte--2017|Hallegatte and Rozenberg, 2017]] ).

There is ''robust evidence'' that economic development based on the exploitation of natural resources can significantly increase the vulnerability of communities at the local level. For example, there is a correlation between political arrangements and environmental degradation that brings about both disasters and an increase in disaster risk ( [[#Cannon--2010|Cannon and Müller-Mahn, 2010]] ; [[#Pereira--2020|Pereira et al., 2020]] ), while development is recognised by some as a key element for adaptation ( [[#Cannon--2010|Cannon and Müller-Mahn, 2010]] ).

Maladaptation is an important thread given its relevance to assess ways that well-intentioned development can exacerbate past and existing vulnerabilities and undermine livelihoods (see [[#8.2|Section 8.2.2.1]] ). Evidence shows that some local development projects can undermine resilience and increase the vulnerability of neighbouring communities, leading to maladaptation ( [[#Magnan--2016|Magnan et al., 2016]] ; [[#Schipper--2020|Schipper, 2020]] ; [[#Eriksen--2021|Eriksen et al., 2021]] ). Development projects can also negatively affect the vulnerability and create new ones of the very community where they are implemented ( [[#Burby--2006|Burby, 2006]] ; [[#Magnan--2016|Magnan et al., 2016]] ; [[#Atteridge--2018|Atteridge and Remling, 2018]] ; [[#Thomas--2019|Thomas and Warner, 2019]] ; [[#Work--2019|Work et al., 2019]] ). Maladaptation has also received growing attention since AR5 as a projected future climate risk for vulnerable social groups (see [[#8.4.5.5|Section 8.4.5.5]] ) and in the context of adaptation constraints and trade-offs in climate resilient development (see Sections 8.5.1; 8.6.1) '','' Despite maladaptation, there is however ''robust evidence'' that inclusive and sustainable development at the local level, can reduce vulnerability ( [[#Cannon--2010|Cannon and Müller-Mahn, 2010]] ; [[#Patnaik--2019|Patnaik et al., 2019]] ).

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<span id="economic-and-non-economic-losses-and-their-relevance-for-poverty-and-livelihoods"></span>
==== 8.3.3.2 Economic and Non-economic Losses and their Relevance for Poverty and Livelihoods ====

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Economic losses include income and physical assets and non-economic losses include mortality, mobility and mental well-being losses from climate change (see [[#8.3.4|Section 8.3.4]] ). The IPCC WGII AR5 ( [[#IPCC--2014a|IPCC, 2014a]] ) primarily associated L&amp;Ds with extreme weather events and economic impacts, and treated it primarily as a future risk. New evidence provides insights into present-day L&amp;Ds from slow-onset impacts (e.g., sea level rise) ( [[#Adamo--2021|Adamo et al., 2021]] ) and non-economic losses (e.g., cultural impacts, emotional and psychological distress) ( [[#McNamara--2021b|McNamara et al., 2021b]] ) which previously received much less attention. AR5 had more focus on L&amp;Ds in high-income regions than in regions most at risk, such as SIDS and least developed countries (LDCs) ( [[#van%20der%20Geest--2020|van der Geest and Warner, 2020]] ).

Impacts of climate change are affecting the economic and non-economic dimensions of people’s lives, including subsistence practices of communities that are experiencing decreases in agriculture productivity and quality, water stress, increases in pests and diseases, disruption to culture, and emotional and psychological distress, to cite just a few ( [[#Savo--2016|Savo et al., 2016]] ). For example, the cumulative effects of slow-onset events threaten food security especially among the poor in Latin America and the Caribbean—regions which face the largest gender gap in terms of food security globally ( [[#Zuñiga--2021|Zuñiga et al., 2021]] ). In general for Global South countries, the global average temperature warming (including the Paris target of 1.5°C) means substantially higher warming and including higher frequency and magnitude of extreme events, that will result in significant impacts on societal vulnerability ( [[#Aitsi-Selmi--2016|Aitsi-Selmi and Murray, 2016]] ; [[#Djalante--2019|Djalante, 2019]] ).

Measuring losses from climate change impacts in terms of poverty and inequality can be difficult, and part of the lack of assessments of non-economic L&amp;D can be attributed to the limited observational climate data on poor countries and population impacted, which are mostly concentrated in the Southern Hemisphere ( [[#Roy--2018|Roy et al., 2018]] ).This is also due to the challenges posed by the limited data available for assessing attribution ( [[#Cramer--2014|Cramer et al., 2014]] ; Harrington and [[#Otto--2020|Otto, 2020]] ; [[#Otto--2020a|Otto et al., 2020a]] ) and lack of a comprehensive set of adaptation metrics ( [[#Otto--2020b|Otto et al., 2020b]] ). Economic L&amp;Ds from climate change are often assessed and reported after disasters or within crises, however, non-economic losses from climate change are often overlooked as is their relevance for poverty and livelihoods. For those who experience both economic and non-economic losses, the impacts of climate change are very real and profound ( [[#Tschakert--2017|Tschakert et al., 2017]] ; [[#Roy--2018|Roy et al., 2018]] ) Particularly in low-income and most vulnerable regions, it is not the absolute economic loss, but the combination of economic and especially non-economic losses that need to receive higher attention and need to inform adaptation strategies.

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<span id="observed-disproportionate-impacts-according-to-economic-and-non-economic-losses-and-damages-due-to-climate-change"></span>
=== 8.3.4 Observed Disproportionate Impacts According to Economic and Non-economic Losses and Damages Due to Climate Change ===

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Since AR5 a new discourse on L&amp;D has emerged with new typology and elaboration of a definition. L&amp;D has a long and contentious history and is enshrined in the Paris Agreement (see Cross-Chapter Box LOSS in Chapter 17). Despite ambiguity about what constitutes L&amp;D ( [[#Boyd--2017|Boyd et al., 2017]] ), it focuses on how to avert, minimise, and address the negative impacts of climate change, including those that cannot be avoided through adaptation. It can also be thought of as the observed residual risk (and potentially irreversible losses) from climate change when adaptation limits are encountered and mitigation has failed ( [[#Boda--2020|Boda et al., 2020]] ). L&amp;D is considered a policy mechanism (see Cross-Chapter Box LOSS in Chapter 17). There is also a burgeoning science for L&amp;D ( [[#Mechler--2019b|Mechler et al., 2019b]] ) which advances the breakdown on compounding vulnerabilities and highlights the disproportionate effects of climate change on the vulnerable and marginal (see Box 8.5 for illustration of distributional effect of both the drought and responses in the Cape region in South Africa). New evidence provides additional insight into L&amp;D from slow-onset events related to climate change (sea level rise, drought) (see [[#Anjum--2021|Anjum and Fraser, 2021]] ; [[#Lund--2021|Lund, 2021]] ) For example, ( [[#Singh--2021|Singh et al., 2021]] ) found growing evidence of urban droughts leading to economic losses (e.g., groundwater overextraction, financial impacts) and non-economic losses (e.g., conflict, increased drudgery).

The literature is assessed according to this new L&amp;D typology, which includes both extreme and slow-onset events and has a strong emphasis on climate justice and disproportionate impacts of climate hazards (see Figure 8.3), with a new focus non-economic L&amp;D.

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==== 8.3.4.1 Economic (e.g., Income, Assets) Impacts of Climate Change and Vulnerability ====

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While extreme events are not new, the intensity and frequency of extreme events are stacking, leading to additional increase in poverty or vulnerability in some regions, exacerbated by COVID-19, and up against existing development pathways leading to significant impact on economic losses globally ( ''high confidence'' ). There is ''robust evidence'' that many African countries experience climate-related losses in terms of loss of crop yields, destroyed homes, food insecurity through increased food prices, and displacement (Box 8.5; [[#Olsson--2014|Olsson et al., 2014]] ). Attention has been focused on low-income groups, women and children, poor rural communities, and Indigenous Peoples such as the example of the Dupong, an Indigenous Peoples in Ghana using Indigenous strategies to limit adverse impacts of climate change-induced water shortages ( [[#Opare--2018|Opare, 2018]] ). In Kenya, economic L&amp;D during droughts between 2009 and 2011 incurred costs that included trucking emergency water and food supplies, and loss of livestock and livelihoods. Cross-sectoral economic effects were estimated to reduce gross domestic product (GDP) by 2.8% yr −1 ( [[#King-Okumu--2021|King-Okumu et al., 2021]] ). Past studies have similarly shown that in the context of extreme events, such as floods or droughts, the most commonly sold assets are livestock and land. The sale of property particularly reduces the asset base, creates long-term vulnerabilities to future events and can trigger chronic poverty ''(high confidence'' ). People may face food shortages in the future from lack of crop production ( [[#Opondo--2013|Opondo, 2013]] ).The sale of cattle affects the household asset base, as well as important access to animal traction power for farming.

In South Asia, there is ''robust evidence'' of economic impacts of climate change ( [[#Cao--2021|Cao et al., 2021]] ), for example in the Sundarbans (a transboundary ecosystem with components in both India and Bangladesh, with the problem of unproductive livelihoods being common across residents of both countries) observations show local livelihoods are rapidly becoming unproductive (loss of fish, and increasing salination making agriculture increasingly difficult) ( [[#Ghosh--2018|Ghosh, 2018]] ); conditions that are exacerbated by climate change impacts ( ''high confidence'' ). Cyclone and storm surges induced by climate change force saline water into agricultural lands along the coast, which damages crops not only in the year the cyclone hits, but for several years afterwards ( [[#Rabbani--2013|Rabbani et al., 2013]] ). They showed in Shyamnagar Upazilla in Satkhira district the proportion of salinity-free farmland has gone down over the past 20 years, from more than 60% to nil ( [[#Rabbani--2013|Rabbani et al., 2013]] ). Vietnam has also experienced effects of flooding and salinisation in the Mekong delta, coupled with rapid social development. Intensified floods and droughts have dramatically resulted in loss of livelihoods in agriculture and fisheries in some areas of the basin ( [[#Evers--2018|Evers and Pathirana, 2018]] ). In Vietnam, the expected salinisation increases livelihood shifts into areas that are riskier, such as shrimp farming. Furthermore, the Vietnamese Mekong Delta is characterised by strong migration processes towards cities, particularly Ho Chi Min, meaning that abrupt livelihood shifts are already happening. There are emerging examples of Indigenous Peoples affected by climate change in indigenous farming mountain communities of the Nepal Himalaya. ( [[#Sujakhu--2019|Sujakhu et al., 2019]] ). The Philippines has experienced extreme events, such as Typhoon Haiyan in 2013, which left more than 7353 people reported dead or missing, damaged or swept away more than 1.1 million houses and injured more than 27,000 people ( [[#McPherson--2015|McPherson et al., 2015]] ). More than 4 million were displaced. The cost of damages has been estimated at USD 864 million with USD 435 million for infrastructure and USD 440 million for agriculture in affected regions ( [[#McPherson--2015|McPherson et al., 2015]] ).

Sea level rise, coastal flooding and surge inundation are increasingly pressing problems across the urban Pacific, including the urban and coastal population of Vanuatu ( [[#McDonnell--2021|McDonnell, 2021]] ). Pacific region islands, such as Vanuatu ( [[#Handmer--2019|Handmer and Nalau, 2019]] ), are particularly vulnerable to climate change. Kiribati and Tuvalu are impacted by exceptionally high tides that affect the urban atolls of South Tarawa and Funafuti, and cyclonic activity causing extensive economic damage in Tuvalu ( [[#Curtain--2019|Curtain and Dornan, 2019]] ). Limited migration opportunities for low-income households can result in forced immobility, and high tides, sea level rise and cyclonic damages could result in relocation of significant groups of the population.

A pertinent example of economic losses is the example of the Torres Strait in Australia. This example shows evidence of communities living on remote islands. Boigu is a low-lying mud island inundated by the sea during high tides and storm surges. Those most exposed and vulnerable to climate change have limited livelihood assets and face challenges to secure external support with government and others. Place-based values evoke a reluctance to relocate or retreat with economic losses such as community infrastructure, housing and cultural sites ( [[#McNamara--2017|McNamara et al., 2017]] ). In the Great Barrier Reef, Australia sea level rise and sea level global temperature warming affects fisheries’ productivity and tourism ( [[#Evans--2016|Evans et al., 2016]] ). Unprecedented burn area of wild forest fires in Australia between September 2019 and January 2020 ( [[#Boer--2020|Boer et al., 2020]] ) extended over almost 19 million hectares, destroyed over 3000 houses and killed 33 people ( [[#Filkov--2020|Filkov et al., 2020]] ).

The 2018 European heatwave in Northern and Eastern Europe caused multiple and simultaneous crop failures—among the highest observed in recent decades ( ''high agreement'' ). These yield losses were associated with extremely low rainfall in combination with high temperatures between March and August 2018 ( [[#Beillouin--2020|Beillouin et al., 2020]] ). Across Europe, in 2018, people experienced one of the worst harvests in a generation. Northern and Eastern Europe experienced multiple and simultaneous crop failures—among the highest observed in recent decades. These yield losses were associated with extremely low rainfalls in combination with high temperatures between March and August 2018. This compounding of extreme conditions in 2018 led to one of the highest negative relative yield anomalies at the scale of Eastern and Northern Europe, across a large array of crop species ( [[#Beillouin--2020|Beillouin et al., 2020]] ).

Extreme climate events are disproportionately impacting economies of the most vulnerable everywhere ( ''medium evidence, high agreement'' ). In the Caribbean, Central America and USA, Hurricanes Katrina, Harvey, Irma, Maria and Michael are examples of extreme climate events that have displaced households, destroyed homes, and led to loss of income among the poor and marginalised ( [[#Klinenberg--2020|Klinenberg et al., 2020]] ). Puerto Rico was devastated by Hurricane Maria but received less support from the Federal Emergency Management Agency ( [[#García--2021|García, 2021]] ). Evidence is emerging on unequal governance responses in the USA versus Puerto Rico ( [[#Joseph--2020|Joseph et al., 2020]] ). Floods, storms and heatwaves have impacted poorer communities and wildfires in California have impacted many wealthy groups, and also infrastructure used by all, for example, with lengthy electrical power blackouts. However, they have particularly impacted those vulnerable to disasters, such as undocumented Latino/a and Indigenous immigrants in the case of the Thomas Fire in California’s Ventura and Santa Barbara counties ( [[#Méndez--2020|Méndez et al., 2020]] ). In 2017, Hurricane Irma hit Ragged Island in the Bahamas as a category 5 storm, leaving the island in ruins and deemed ‘unliveable’ by its authorities, with most infrastructure left as rubble, no essential utilities remained, schools and health clinics were in ruins and the stench of dead animals was overwhelming. This storm resulted in significant economic L&amp;D by the community through loss of their homes, churches, schools, agricultural land and infrastructure ( [[#Thomas--2020|Thomas and Benjamin, 2020]] ).

Across South America, groups of farmers, children, elderly, Indigenous Peoples and traditional communities are increasingly exposed to floods, droughts, wild forest fires and losses in crop yields, resulting in significant economic costs ( ''medium evidence, high agreement'' ) (see Box 8.6). Urban communities, in particular those living in informal settlements, are exposed to heatwaves. In Peru, analysis of water risks posed by climate change in the Vilcanota-Urubamba basin, Southern Peru, revealed seasonal water scarcity and glacial lake outburst floods (GLOFs), pose a serious threat for highly exposed and vulnerable people. It showed that very high-risk potentials of 134 current and another 6 out of 20 future glacier lakes as potentially highly susceptible to outburst floods. A total of eight existing and one possible future lakes indicate future river discharge could be reduced by some 2–11% (7–14%) until 2050 (2100). Farmers, in particular smallholders, in some regions face losses to irrigated agriculture and hydropower capacity with effects on water scarcity and food security ( [[#Drenkhan--2019|Drenkhan et al., 2019]] ). However, other assessments also point towards positive effects of water reservoirs and hydropower in terms of water storage, flood management and irrigation ( [[#Ahmad--2014|Ahmad et al., 2014]] ; [[#Liu--2015|Liu et al., 2015]] ; [[#Kuraku--2019|Kuraku et al., 2019]] )

There are additional dimensions of economic losses that are of a more diffuse nature. In particular, climate change is also expected to negatively affect labour supply, particularly in temperature exposed industries (agriculture, mining, manufacturing, construction), due to increases in the number of extreme hot days ( [[#Graff%20Zivin--2014|Graff Zivin and Neidell, 2014]] ; [[#Garg--2020|Garg et al., 2020]] ). Low-income countries have on average a large share of workers in such industries and will thus be especially hard hit. Aside from labour supply, a number of studies also document negative impacts to manufacturing productivity ( [[#Acharya--2018|Acharya et al., 2018]] ; [[#Pogacar--2018|Pogacar et al., 2018]] ; [[#Somanathan--2021|Somanathan et al., 2021]] ). These findings provide a channel to explain macroeconomic consequences of climate change ( [[#Burke--2015|Burke et al., 2015]] ). However, there are also non-economic costs in that extreme heat will cause increased discomfort to workers, such as psychological stress, disease and in extreme cases, death among the workforce in developing economies, as well as tropical and sub-tropical countries ( [[#Ansah--2021|Ansah et al., 2021]] ).

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<span id="non-economic-loss-and-damage-e.g.-mobility-well-being"></span>
==== 8.3.4.2 Non-economic loss and damage (e.g., Mobility, Well-being) ====

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Climate change L&amp;D presents an existential threat to some ( [[#Boyd--2017|Boyd et al., 2017]] ). For example, Pacific Island countries have contributed least to total GHG emissions, but the nations of the South Pacific are highly vulnerable to rising sea levels, tropical cyclones and other climate-related risks ( [[#Nand--2020|Nand and Bardsley, 2020]] ). For example, across Oceania there is significant risk that sea level rise will lead to forced relocation. Pacific leaders underscore the importance of losses, including deep connections between their world views and their land, and that leaving their islands can only be considered an option of ‘last resort’ ( [[#McDonnell--2021|McDonnell, 2021]] ).

Non-economic loss and damage (NELD) is values based (subjective and intangible) and relates to norms, social values and highlights intersectional experiences and perspectives on climate risk. The discourse on L&amp;D includes a framing of NELD as loss of human and non-human life, and mental and physical health that is experienced widely across the world in vastly different ways associated with social values ( [[#Tschakert--2019|Tschakert et al., 2019]] ). There are respectable arguments for the case that all life has intrinsic value ( [[#Vetlesen--2019|Vetlesen, 2019]] ). The NELD framing of climate impacts highlights that not all risks are measurable. While difficult to measure, there are a growing number of cases of NELD globally ( ''medium evidence, high agreement'' ). Illustrative examples of NELD from climate change include the Pacific ( [[#McNamara--2021b|McNamara et al., 2021b]] ) and SIDS in the Caribbean. ( [[#Martyr-Koller--2021|Martyr-Koller et al., 2021]] ). For example, the hurricane season in 2017 was particularly extreme resulting in climate-induced displacement with direct implications for NELD, including threats to health and well-being and loss of culture and agency ( [[#Thomas--2020|Thomas and Benjamin, 2020]] ).

In the context of the Pacific Islands, NELDs are thought of as interconnected and span human mobility and territory, cultural heritage and Indigenous knowledge, life and health, biodiversity and ecosystem services, and sense of place and social cohesion ( [[#Carmona--2017|Carmona et al., 2017]] ; [[#Ojwang--2017|Ojwang et al., 2017]] ; [[#McNamara--2021b|McNamara et al., 2021b]] ). There are gaps in our understanding of NELD, much of the evidence is from the Global South and at smaller scales ( ''high agreement'' ), NELD is not explicitly linked to attribution science yet and evidence often lacks coverage on certain groups ( [[#Boyd--2017|Boyd et al., 2017]] ; [[#Carmona--2017|Carmona et al., 2017]] ; [[#Ojwang--2017|Ojwang et al., 2017]] ). Non-economic losses are often associated with displacements and migration in terms of climate change and human vulnerability ( [[#8.2.1.4|Section 8.2.1.4]] ), studies show that the impacts of extreme flooding, droughts and/or hurricanes and cyclones that can lead to a sense of lost identity and place, and emotional distress, that are hardly assessed dimensions of impacts and risks ( [[#Adger--2014|Adger et al., 2014]] ; [[#Barnett--2016|Barnett et al., 2016]] ; [[#Tschakert--2017|Tschakert et al., 2017]] ; [[#Serdeczny--2018|Serdeczny et al., 2018]] ). Non-economic losses are particularly relevant for understanding adverse consequences of climate change on the poor and most vulnerable population groups ( ''high confidence'' ). These NELD categories are still overlooked in vulnerability assessments and adaptation planning. A novel way to consider NELD in assessments is to interconnect with a sustainable development perspective ( [[#Boyd--2017|Boyd et al., 2017]] ; [[#Boda--2020|Boda et al., 2020]] ).

In order to categorise the different types of NELD that exist, ( [[#Serdeczny--2016|Serdeczny et al., 2016]] ), reviewed the literature and came up with a set of systematic categories that capture what is usually thought about as having intrinsic value and according this framing of NELD this includes: human life, sense of place and mobility, cultural artefacts, biodiversity and ecosystems, communal and production sites and agency and identity ( [[#Serdeczny--2016|Serdeczny et al., 2016]] ; [[#Serdeczny--2019|Serdeczny, 2019]] ). For example, there is emerging evidence on linkages between slow-onset events and mobility decisions, trajectories and outcomes ( [[#Zickgraf--2021|Zickgraf, 2021]] ). In addition, categories include psychosocial and emotional distress ( [[#van%20Der%20Geest--2016|van Der Geest and Schindler, 2016]] ). For example, research shows potential increased risk of intimate partner violence following disasters, noting that societies that are vulnerable to climate change may need to prepare for the social disasters that can accompany disasters revealed by natural hazards (Malik and Stolove, 2017; [[#Rai--2021|Rai et al., 2021]] ).

Geographical focus on non-economic losses in the literature is largely on the Global South with studies mainly smaller in scale ( ''high agreement'' ). Many events studied include severe storms, floods and landslides. Key groups affected include low-income groups, agro-pastoralists, women and girls, children and youth, Indigenous Peoples, ethnic and religious minorities. In Europe, the Samis face significant challenges to health as ecosystems deteriorate ( [[#Jaakkola--2018|Jaakkola et al., 2018]] ). In Zimbabwe, Storm Idai affected 270,000 people and subsequent flooding and landslides left 340 people dead and many others missing ( [[#Chanza--2020|Chanza et al., 2020]] ). There is evidence of loss of cultural heritage sites due to sea level rise and coastal erosion as well as other climate variability ( [[#Brooks--2020|Brooks et al., 2020]] ). Haile et al. (2013) show flood casualties in Ethiopia include children drowned while playing outside during the 2007 flood period although official data is hard to come by (p. 489). Moreover, loss of place was experienced in Itang, where many of the local houses are built from wood, grasses and mud walls, which are easy to reconstruct, but are not strong enough to withstand an extreme flood. Here, 38% of the surveyed houses were severely damaged by the 2007 flood. These houses were constructed as an adaptation strategy but could not withstand the floods. In Kenya, [[#Opondo--2013|Opondo (2013)]] shows loss of human life was the most severe impact of floods. For example, in the focus group discussion with men, ‘it was reported that a boat capsized on River Nzoia at Siginga and ten people died’ (p. 457). In Mozambique, Brida et al. (2013) show loss of sense of place occurred after flooding in the central districts of Caia and Mopeia, which had a devastating impact on homes and livestock ( [[#Brida--2013|Brida et al., 2013]] ). Health impacts of the forest fires in Amazon basin countries have disproportionately affected vulnerable people and social groups (see Box 8.6).

In the literature on NELD, there are many examples of loss of life ( ''high agreement'' ). In Nepal, one of the deadliest landslides in Nepal history resulted in the death of 156 people ( [[#van%20der%20Geest--2018|van der Geest, 2018]] ). Evidence from Landslide Jure and consecutive rainfall in Sindhupalchok in Nepal showed the experience led to mental stress, such as fear of new landslides, in about 68.4% of people interviewed ( [[#van%20Der%20Geest--2016|van Der Geest and Schindler, 2016]] ). One study in Nepal showed that almost a quarter (23%) of the households interviewed had sold property, including homes, livestock and heirlooms in response to flooding ( [[#Bauer--2013|Bauer, 2013]] ). Human deaths are increasingly associated with L&amp;Ds from tropical cyclones and typhoons, such as in the southern coastal districts of Bangladesh, in particular Khulna and Satkhira ( [[#Chiba--2017|Chiba et al., 2017]] ). A case study from Mindanao, Philippines, by [[#Chandra--2017|Chandra et al. (2017)]] also reported physical injuries and loss of life from the most powerful typhoon for over a century in 2012, affecting more than 6 million people and killing at least 1000 people ( [[#Eugenio--2016|Eugenio et al., 2016]] ). Beckman and Nguyen (2016) reported that in Vietnam floods in 2004 washed away 24 houses in the commune, with the loss of families when their houses were washed away.

An illustrative example is climate-induced loss of well-being and (im)mobility in Bhola Slum, an informal settlement in Dhaka, Bangladesh. Research revealed that IDPs from the southern coast experienced loss of belonging, identity, quality of life and social value produced in people a nostalgia and desire to return home ( [[#Ayeb-Karlsson--2020|Ayeb-Karlsson et al., 2020]] ). Another example is of urban climate change justice experienced by migrants in the Indian cities of Bengaluru and Surat, where environmental marginalisation can be attributed to a lack of recognition of citizenship rights and informal livelihood strategies driven by broken social networks and a lack of political voice, as well as heightened exposure to emerging climate risks and economic precariousness. In this case, migrants experience extreme forms of climate injustice in their invisibility to formal government and are even actively erased from cities through force or discriminatory development policies ( [[#Chu--2019|Chu and Michael, 2019]] ).

NELD also includes the loss of social networks. This has lasting implications for psychological health as well as for coping with crises following disasters or challenges posed by adverse climate change impacts. For example, many households in villages affected by Cyclone Aila in Dacope and Koyra upazilas of Khulna district in Bangladesh migrated to other places permanently after the cyclone, as these affected villages were subject to long-term flooding (e.g., 2–3 years) following the cyclone. They migrated as they were unable to restore their livelihoods and, thus, were unable to secure necessary income for survival ( [[#Saha--2017|Saha, 2017]] ).

The examples show the multifaceted nature of intangible and non-economic losses that people experience in the context of climate change and the daily risks they are exposed to. Conventional vulnerability assessments cover some aspects that are linked to the likelihood of experiencing non-economic losses, such as aspects of health, governance, education and in some cases also forced migration and the role of social networks. Overall, the elements of this assessment here underscore that it is not just the climatic stressor, but rather the underlying context conditions that decide whether an extreme event translates into a disaster.

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<span id="economic-and-non-economic-losses-and-damages-due-to-climate-change-and-their-implications-for-livelihoods-and-livelihood-shifts"></span>
=== 8.3.5 Economic and Non-economic Losses and Damages Due to Climate Change and their Implications for Livelihoods and Livelihood Shifts ===

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This section examines the intersections between L&amp;Ds and livelihood shifts. This requires an examination of the differentiated aspects of livelihoods. Understanding economic (e.g., loss of food crops, infrastructure, assets etc.) and non-economic losses (e.g., health, well-being, loss of place, agency) and their consequences for livelihoods is important that the intangible aspects become clearly visible and receive greater attention in loss assessments and in designing adaptation strategies and programmes. Figure 8.10 provides a summary of examples of observed impacts of climate hazards on economic and non-economic capital and the section assesses livelihood implications across regions. It shows examples of climate hazards attributed to climate change in studies since AR5, across a range of geographical sites for heatwaves, drought, hurricanes, and floods and non-economic L&amp;Ds. Figure 8.10 reveals examples of climate hazards attributed to climate change in studies since AR5 across a range of geographical sites for extreme and slow-onset events, such as heatwaves, drought, hurricanes and sea level rise. These are associated with non-economic L&amp;Ds. The figure underscores that non-economic L&amp;Ds lead to significant livelihood threats and livelihood changes. In addition, the limits of adaptation become evident (Chapter 16).

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[[File:cec5de2408aa69eb98d96c2902d89ee8 IPCC_AR6_WGII_Figure_8_010.png]]

'''Figure 8.10 |''' '''Examples of non-economic loss and damage associated with climate hazards attributed to climate change against a background of global vulnerability.''' Symbols with corresponding detail in the table show examples where non-economic losses have been documented. The figure is not exhaustive in terms of examples of extreme or slow-onset events or losses. It does not capture undocumented cases. It is an illustration of the relationship between unequivocal human-induced climate change and intangible losses (Adapted from Boyd et al., 2021).

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==== 8.3.5.1 Livelihood Shifts Resulting from L&amp;D from Climate Change ====

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While there are limited studies that view economic and NELD from climate change at a global scale of livelihood transformations there is ''robust evidence'' on the granular linkages, at community, national and regional levels, between losses, coping strategies and livelihood shifts. Across Africa, climate change is impacting crop yields and destroying homes, resulting in loss of infrastructure and leading to non-economic losses associated with involuntary migration and displacement ( [[#Olsson--2014|Olsson et al., 2014]] ), and loss of livestock and assets (see IPCC SR 1.5°C, Chapter 3, ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ), resulting in long-term reduction in the capacity for agriculture and land management. For example, in March 2019 Tropical Cyclone Idai in Mozambique, Zimbabwe and Malawi led to substantial losses of agriculture, infrastructure, and access to aid and support, all of which contributed to significant displacement in each country ( [[#Fischel%20de%20Andrade--2021|Fischel de Andrade and de Lima Madureira, 2021]] ). Examples of livelihood impacts include livelihood shifts among Kenyan pastoralists to camel husbandry, resulting from household inequalities in assets and changes in relation to weakening of social norms of reciprocity and social cohesion ( [[#Volpato--2019|Volpato and King, 2019]] ).

Extreme climatic events pose serious disruptions to local livelihoods and asset bases, requiring people to reconstruct, transform and diversify livelihoods ( [[#Uddin--2021|Uddin et al., 2021]] ). Examples of livelihood shifts across Asia and Southeast Asia (e.g., Bangladesh, India, Philippines, Vietnam) include rural communities in coastal areas, urban settlements that are experiencing economic losses ( ''high confidence'' ) from, for example, crop failure and reduced access to fish, which contribute to non-economic losses associated with involuntary migration ( [[#Ghosh--2018|Ghosh, 2018]] ) and the malnutrition of children ( [[#Siddiqi--2011|Siddiqi et al., 2011]] ). For Bangladesh, Chiba et al. (2017) show a connection between mental stress and impacts to the fundamental capacity to sustain livelihoods, such as food and a place to live, due to severe damage to houses, homesteads, properties, livestock and crops, loss of family members and relatives, and anxiousness about securing employment and income in the future. In Bangladesh coastal communities experienced losses in livelihood assets due to Cyclones Sidr and Aila ( [[#Uddin--2021|Uddin et al., 2021]] ) and a significant number of cyclone victims were displaced from their homes by severe cyclones. People have had to change their occupations—both intra- and intersectorally—and are confronted by increased consumption and social costs. The study uncovered differences in impacts between occupations, such as farming and fishing; fishers changed their occupation post-disaster. The study also showed evidence that local people are learning to live with change and uncertainty by nurturing and combining various types of knowledge and social memory, generating diversified livelihood options and self-organising to enhance their resilience to future extreme weather events. In Bangladesh, [[#Ahmed--2019|Ahmed et al. (2019)]] found cyclones, riverbank erosion, salinity intrusion and floods negatively impacted people’s lives by reducing their livelihood options. Their study found that when there are limited adaptation strategies, many people turn to ‘illegal livelihoods’ included using fine mesh nets to collect shrimp fry in the rivers, as well as logging in the Sundarbans. These people include the poorest and vulnerable, and law enforcement only exacerbates their vulnerability. [[#Escarcha--2020|Escarcha et al. (2020)]] , studied impacts of typhoons, floods and droughts on crop production and effects on livelihoods of cash crop focused on rural villages in the Philippines. Their preliminary observations show a shift from crop to livestock production as a buffer activity to recover from crop losses. Farmers changed their farming activities as a multi-adaptive response driven by past experiences of climatic changes, farmers’ social relations, household capacity and resources available.

In Central Asia, the Sahel and South Asia, three global poverty hotspots, change impacts were shown to undermine traditional knowledge about livelihoods in ways that jeopardise future culture cohesion and sense of place ( [[#Tucker--2015|Tucker et al., 2015]] ). [[#Acosta--2016|Acosta et al. (2016)]] identified loss to productive sites in the Philippines with landslides destroying agriculture, leaving many farmers without livelihoods. Similarly, Beckman and Nguyen (2016) in Vietnam identified an example where communal dams had been destroyed in floods leading to lack of irrigation for communal sites and local loss of farmland for farming communities. [[#Chandra--2017|Chandra et al. (2017)]] identified the vicious cycle between declining agricultural production and conditions of soil erosion due to floods and droughts resulting in decreased crop fertility to productive sites with implications for decline in crop yields, loss of crops and of livelihood assets. Climate change-related extreme weather events, such as typhoons, floods, and droughts, can have detrimental impacts on crop production ( ''high confidence'' ) and in the Philippines and Pakistan have significantly affected the livelihoods of cash crop-focused rural villages ( [[#Escarcha--2020|Escarcha et al., 2020]] ; [[#Jamshed--2020b|Jamshed et al., 2020b]] ). There is an emerging shift from crop to livestock production as a buffer activity to recover from crop losses ( [[IPCC:Wg2:Chapter:Chapter-5#5.10.4|Section 5.10.4]] ; [[#Jamshed--2017|Jamshed et al., 2017]] ; [[#Escarcha--2020|Escarcha et al., 2020]] ). As with many examples of livelihood shifts, the viability of the shifts in the long term under climate change have yet to be assessed.

In Africa, many communities already experience drought- and flood-related disasters ( ''high confidence'' ) such as those that negatively impact livelihoods and assets in the Muzarabani district of Zimbabwe ( [[#Mavhura--2017|Mavhura, 2017]] ). In Muzarabani community has revived and developed new livelihood strategies to manage risks, including local informal safety nets, local farming practices and the traditional flood-proofing structures. Food security and agriculture productivity are examples of livelihood resources most at risk to climate hazards (see Figure 8.2) ( ''high confidence'' ). An illustration of such risks to cocoa farmers in Ghana includes increased incidences of crop pests and diseases, wilting of cocoa leaves, high mortality of cocoa seedlings which affected expansion and farm rehabilitation, and wilting of cherelles resulting in losses of crop yield. An illustration of livelihood shifts resulting from losses is of farmers shifting to cereals due to the unpredictable climatic patterns and the shortened duration of rainfall. Yet, insecurity with storage, supply chains and low returns from cereal production, coupled with land scarcity in the Western region, have resulted in a return to cocoa production ( [[#Asante--2017|Asante et al., 2017]] ).

Research from Australia shows complex linkages between the impacts of drought on livelihood income, health and cultural heritage, increasing risk of heat stroke, and possibly a link to suicide among male farmers ( [[#Alston--2012|Alston, 2012]] ; [[#Hanigan--2012|Hanigan et al., 2012]] ; [[#Marshall--2019|Marshall et al., 2019]] ). The link between agricultural losses and suicides has also been noted in South Asia, including India ( [[#Carleton--2017|Carleton, 2017]] ). Livelihoods are shifting with impacts to well-being, as noted by ( [[#Evans--2016|Evans et al., 2016]] ), who showed connections between loss of fishery productivity and impact on tourism sector livelihoods in the Great Barrier Reef region. In Europe, losses to Indigenous Peoples are associated with loss of well-being of Sami communities and has forced livelihood shifts from reindeer herding due to loss of ecosystems to support the animals ( [[#Persson--2017|Persson et al., 2017]] ; [[#Jaakkola--2018|Jaakkola et al., 2018]] ). Traditional pastoralist systems are also greatly impacted by cumulative dual challenges of encroachment of other land users and by climate change. Traditional Sami reindeer herding strategies are still practiced, but the rapidly changing environmental circumstances are forcing herders into uncharted territories where traditional strategies and the transmission of knowledge between generations may be of limited use. For example, rotational grazing is no longer possible as all pastures are being used, and changes in climate result in unpredictable weather patterns unknown to earlier generations ( [[#Axelsson-Linkowski--2020|Axelsson-Linkowski et al., 2020]] ). These examples show that there are complex factors underpinning the linking L&amp;D and shifting livelihoods. Moreover, there are significant challenges to undertaking a shift to secure alternative livelihoods.

Linkages between losses, coping strategies and livelihood shifts in small islands (e.g., in the Pacific region, Kiribati and Tuvalu, and in the Caribbean, the Bahamas) shed light on impacted low-income households. For example, farmers have experienced extensive damage to homes and loss of infrastructure, and experience lack of migration opportunities ( [[#Curtain--2019|Curtain and Dornan, 2019]] ). Evidence is growing that there is also significant loss of cultural heritage in resettlement ( [[#Barnett--2012|Barnett and O’neill, 2012]] ), evidence from small islands’ displaced communities suggests that resettlement can have impacts on sense of place, identity and social fabric, a theme highly relevant to loss, coping and adapting livelihoods, and not only restricted to small islands ( [[#McNamara--2021b|McNamara et al., 2021b]] ). Roberts (2015) identified loss of communal sites in Kiribati. It is predicted that, by 2050, up to 80% of the land on the island of Buariki and 50% of the land on Bikenibeu may be completely inundated and these effects will result in significant loss of livelihoods and displacement. Throughout the Caribbean, evidence indicates that there will be an overall reduction in the area of land suitable for crop cultivation, as the region’s climate gets progressively warmer and as rainfall becomes more variable ( [[#Rhiney--2016|Rhiney et al., 2016]] ).

The multiple shocks of extreme events reduce crop yields, destroy homes, and lead to loss of infrastructure and displacement ( ''high confidence'' ). These are experienced in South and North America. For example, in Peru, glacial outbursts have led to loss of livelihoods ( [[#Drenkhan--2019|Drenkhan et al., 2019]] ). People use a range of coping and adaptation strategies to deal with hazards where they live, such as shifting livelihood activities, inputs or production areas. However, traditional techniques are increasingly failing due to changing weather patterns. Across Peru, findings demonstrate that people use temporary and permanent migration among their many coping and adaptation strategies. Hazards related to water excess have been the key force in destroying homes and driving displacement in Peru. In contrast, studies demonstrate that water scarcity also threatens livelihoods and thereby influences migration in Peru. While non-climatic reasons for moving dominate migrants’ motivations in many areas of Peru, water-related climatic drivers of migration are becoming increasingly relevant ( [[#Wrathall--2014|Wrathall et al., 2014]] ). Peru’s smallholder farmers and urban poor are not responsible for the climate crisis, yet their lives and cultural heritage are being increasingly jeopardised by its effects, making improvements in governance an imperative for Peru ( [[#Bergmann--2021|Bergmann et al., 2021]] ). Another area of significance is coffee production in Brazil, where the majority of Brazilian coffee farms are operated by smallholders, producers with relatively small properties, who are mostly reliant on family labour ( [[#Koh--2020|Koh et al., 2020]] ). In the USA (e.g., New Orleans and Puerto Rico), people have lost livelihoods due to displaced households and destroyed homes, leading to loss of income, as well as loss of social networks and family networks and loss of cultural heritage. For example, impacts of Hurricane Katrina have led to people being displaced from their employment, many evacuees had to relocate to new areas, which disrupted their social networks and placed them in unfamiliar labour markets, resulting in mental health challenges ( [[#Palinkas--2020|Palinkas, 2020]] ). There has also been a ‘climate gentrification’ in parts of New Orleans ( [[#Aune--2020|Aune et al., 2020]] ). Many of those who returned to their pre-Katrina areas had to deal with extensive damage to their homes and to public infrastructure.

In summary, across regions there is an increasing number of examples of observed economic and NELD from climate change. Adaptation measures need to better incorporate actions to tackle the burgeoning negative social, psychological and well-being impacts of climate change ( [[#Barnett--2016|Barnett et al., 2016]] ; Box 8.5). At present, losses from climate change are potentially growing faster than adaptation measures across the globe. It is still uncertain how economic and non-economic losses trigger successful or viable new climate-related livelihood transitions for the poor and people or groups in vulnerable situations in the future (see Sections 8.4.4; 8.4.5). In all likelihoods, economic losses from climate hazards (e.g., drought) will be compounded by many factors including COVID-19 and other vulnerability drivers. For instance, globally, small-scale coffee producers have been destabilised by COVID-19, but also because of a history of recurrent (climate) shocks and structural inequalities, and may have to shift into alternative livelihoods ( [[#Guido--2020|Guido et al., 2020]] ). Coastal communities in Vanuatu have been impacted in the immediate period after COVID-19 showing changes in village populations, loss of cash income and difficulties in accessing food, and have experienced shifting pressures on particular resources and habitats ( [[#Steenbergen--2020|Steenbergen et al., 2020]] ). This trend poses real challenges to equity and sustainability.

In summary, this section has moved beyond the IPCC WGII AR5 in laying out structural elements of vulnerability and climate-related vulnerability hotspots globally, such as poverty, lack of access to basic services, gender inequality and undernourishment. The assessment provides new quantitative evidence about the global spatial distribution of systemic human vulnerability and therewith underscores that various hotspots of countries classified as having very high or high vulnerability emerge in regional clusters. In addition, the number of people living in very highly and highly vulnerable country contexts is significantly higher in some assessments, with even twice as many as the number of people living in countries classified as having low and very low vulnerability. The evidence suggests that statistically relevant differences in fatalities per hazard event are not just a product of the hazard event, but also strongly linked with the level of vulnerability of the region or community exposed. The assessment of non-economic losses has also received little attention in past IPCC Assessment Reports, therefore this section has provided new insights on how (next to measurable economic losses) non-economic losses and intangible losses emerge. These non-economic losses represent an important dimension of societal impacts of climate change that has not sufficiently captured so far within standard damage or post-disaster assessments. Finally, the section provides evidence about the existing adaptation gap in terms of differential vulnerabilities and various non-economic losses already experienced.

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'''Box 8.5 | Western Cape Region in South Africa: drought challenges to equity and sustainability'''
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'''Nature of the drought'''
Between 2015 and 2017, the Western Cape region experienced an unprecedented three consecutive years of below average rainfall, leading to acute water shortages, most prominently in the city of Cape Town ( [[#Sousa--2018|Sousa et al., 2018]] ). Anthropogenic climate change made the drought five to six times more ''likely'' ( [[#Pascale--2020|Pascale et al., 2020]] ; see also AR6 WGI Chapter 10, [[IPCC:Wg2:Chapter:Chapter-10#10.6.2|Section 10.6.2]] ). The severity of the drought presented new challenges to the existing management and governance capacity to ensure equitable and sustainable water service delivery. The city’s water supply infrastructure and demand management practice were unprepared for the ‘rare and severe’ event of three consecutive years of below average rainfall ( [[#Wolski--2018|Wolski, 2018]] ; [[#Muller--2019|Muller, 2019]] ). Despite a potential total storage volume of about 900,000 Ml of water (enough water for around a year and a half of normal usage, after taking evaporation into account), Cape Town’s reservoirs fell from 97% full in 2014 to less than 20% in May 2018 ( [[#Ouweneel--2020|Ouweneel et al., 2020]] ; [[#Cole--2021|Cole et al., 2021]] ). The drought saw residents queue for water as restrictions were imposed together with threats of closure of water provision to households ( [[#Sorensen--2017|Sorensen, 2017]] ; [[#Scheba--2018|Scheba and Millington, 2018]] ). Poor communication in the early stages of the drought ( [[#Hellberg--2020|Hellberg, 2020]] ) and a lack of trust in the administration contributed to a near-panic situation at the threat of ‘Day Zero’ as dams almost ran dry in the first half of 2018 ( [[#Enqvist--2019|Enqvist and Ziervogel, 2019]] ; [[#Simpson--2020c|Simpson et al., 2020c]] ). ‘Day Zero’ was avoided largely through public response, water demand management and the 2018 winter rains ( [[#Sorensen--2017|Sorensen, 2017]] ; [[#Booysen--2019a|Booysen et al., 2019a]] ; [[#Muller--2019|Muller, 2019]] ; [[#Rodina--2019b|Rodina, 2019b]] ; [[#Matikinca--2020|Matikinca et al., 2020]] ). At a household level, responses to the drought showed everyday residents can display unprecedented degrees of resilience ( [[#Sorensen--2017|Sorensen, 2017]] ), including behavioural and attitudinal shifts and technological innovation across the full socioeconomic spectrum ( [[#Ouweneel--2020|Ouweneel et al., 2020]] ). But the private nature of some of these responses extended existing inequality in water access through privileged forms of ‘gated adaptation’ by elites which conventional water governance arrangements were unprepared for ( [[#Simpson--2019b|Simpson et al., 2019b]] ; [[#Simpson--2020a|Simpson et al., 2020a]] ).

These ‘climate gating’ actions, such as drilling boreholes, secured water access for high-income households and companies, but excluded a large proportion of Cape Town’s population who could not afford such private technologies ( [[#Simpson--2019a|Simpson et al., 2019a]] ; [[#Simpson--2020b|Simpson et al., 2020b]] ). These responses were unanticipated by the city administration and compounded fiscal challenges faced by the municipality which could no longer use revenues from high-consumption households to cross-subsidise water for low-income households ( [[#Simpson--2020a|Simpson et al., 2020a]] ). This shift threatened to undermine the sustainability of the municipal fiscus and general water access ( Box 9.8; [[#Simpson--2019a|Simpson et al., 2019a]] ; [[#Simpson--2020a|Simpson et al., 2020a]] ). In order to recover losses, municipal water tariffs for consumers were raised by 26% in 2018 ( [[#Muller--2018|Muller, 2018]] ; [[#Simpson--2019a|Simpson et al., 2019a]] ). In addition to a decline in tourism, median estimations of the overall economic impact of the drought indicate loss of 27.6 billion South African Rand (USD 1.7 billion) translating into 64,810 job losses in the Western Cape, with Cape Town accounting for approximately half of those job losses ( [[#DEDAT--2018|DEDAT, 2018]] ). This had a disproportionate impact on unskilled and semi-skilled workers, particularly for those from low- and middle-income households ( [[#DEDAT--2018|DEDAT, 2018]] ). The drought also exacerbated the potential for sanitation health risks of the urban poor where tens of thousands of people lack access to safely managed sanitation facilities ( [[#Enqvist--2019|Enqvist and Ziervogel, 2019]] ).

The Day Zero Disaster Plan included prioritising and protecting the poor and most vulnerable communities where critical infrastructure and facilities and vulnerable and informal residential areas would remain connected while higher-income residential areas would be cut off ( [[#Cole--2021|Cole et al., 2021]] ). Yet it is important to recognise that pre-existing deficiencies in service delivery meant water access for the urban poor did not change as significantly during the drought, particularly those in informal settlements who collect water from standpipes ( [[#Enqvist--2019|Enqvist and Ziervogel, 2019]] ; [[#Matikinca--2020|Matikinca et al., 2020]] ). For these communities, the negative economic impact of the drought was compounded by the unintended consequences of demand management regulation emanating from the drought response. South Africa ostensibly ensures a constitutional right to water, regardless of ability to pay ( [[#Rodina--2016|Rodina, 2016]] ), 58). Since 2018, however, as a consequence of new water tariffs instituted during the drought, Cape Town residents now have had to ‘prove their poverty’ in order to register as indigent households and access their water right ( [[#Scheba--2018|Scheba and Millington, 2018]] ). Further, since 2007 and with increasing effect during the drought, the municipality has installed approximately 250,000 water management devices as a credit control and, during the drought, also a consumption control measure. As these have been largely installed in low-income homes, this control measure disproportionately affected poor households ( [[#Scheba--2018|Scheba and Millington, 2018]] ; [[#Enqvist--2019|Enqvist and Ziervogel, 2019]] ).

'''Lessons from the drought'''
The effect of communication at different stages in the drought highlights how critical information needs to be provided in a format and language that empowers people to act appropriately and collaboratively ( [[#Muller--2019|Muller, 2019]] ; [[#Rodina--2019b|Rodina, 2019b]] ; [[#Rodina--2019a|Rodina, 2019a]] ). Getting political decisions made in a timely fashion and with public support is a long-standing challenge for managers of urban water supplies ( [[#Muller--2017|Muller, 2017]] ; [[#Muller--2019|Muller, 2019]] ). In Cape Town this was further challenged by dependence on a malfunctioning national department for water supply planning, poor coordination between the spheres of government—city, provincial and national governments—and poor collaboration between political representatives, technical experts and strategic managers ( [[#Madonsela--2019|Madonsela et al., 2019]] ; [[#Nhamo--2019|Nhamo and Agyepong, 2019]] ; [[#Rodina--2019a|Rodina, 2019a]] ; [[#Ziervogel--2019b|Ziervogel, 2019b]] ). This highlights the need to strengthen partnerships and collaboration across sectors and scales of governance ( [[#Ziervogel--2019a|Ziervogel, 2019a]] ), including the adoption of a ‘whole-of-society’ approach that recognises the contributions of non-state actors as adopted in the Cape Town Resilience Strategy ( [[#CoCT--2019|CoCT, 2019]] ; [[#Simpson--2020a|Simpson et al., 2020a]] ). Experienced yet inflexible water management initially operated at a distance from politicians and their citizens. There was limited knowledge and capacity in how various municipal departments thought about risk, exposure and vulnerability of Cape Town’s highly differentiated population ( [[#Mukheibir--2007|Mukheibir and Ziervogel, 2007]] ; [[#Pasquini--2015|Pasquini et al., 2015]] ; [[#Madonsela--2019|Madonsela et al., 2019]] ). In the later stages of the drought, Cape Town’s water management department was able to work collaboratively across different departments and with politicians to implement responses.

The Cape Town case highlights how disaster planning for slow-onset city-wide shocks will be become increasingly important to safeguard equity and sustainability across African cities ( [[#Cole--2021|Cole et al., 2021]] ). It demonstrates the importance of integrating state and non-state responses to climate change in municipal adaptation and disaster planning ( [[#Booysen--2019a|Booysen et al., 2019a]] ; [[#Booysen--2019b|Booysen et al., 2019b]] ; [[#Simpson--2020a|Simpson et al., 2020a]] ), particularly for responses with unintended consequences. Further, water tariff models need to be flexible enough and have built-in redundancies in order to prioritise the needs of the urban poor and ensure climate responses do not disproportionately affect low-income groups and deepen existing inequalities ( [[#Scheba--2018|Scheba and Millington, 2018]] ; [[#Enqvist--2019|Enqvist and Ziervogel, 2019]] ; [[#Simpson--2019b|Simpson et al., 2019b]] ). Systems and relationships of mutual accountability can also build more effective water management between spheres of government and enhance horizontal collaboration between municipal departments and non-state entities ( [[#Ziervogel--2019b|Ziervogel, 2019b]] ; [[#Ziervogel--2019a|Ziervogel, 2019a]] ).

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Box 8.5

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